Short-term traffic forecasting overview of objectives and methods

A Simple and Effective Method for PredictingTravel Times on FreewaysJohn Rice and Erik van ZwetAbstract—We present a method to predict the time that will be needed to traverse a given section of a freeway when the depar-ture is at a given time in the future.The prediction is done on the basis of the current traffic situation in combination with historical data.We argue that,for our purposes,the current traffic situa-tion of a section of a freeway is well summarized by the current status travel time.This is the travel time that would result if one were to depart immediately and no significant changes in traffic would occur.This current status travel time can be estimated from single-or double-loop detectors,video data,probe vehicles,or any other means.Our prediction method arises from the empirical ob-servation that there exists a linear relationship between any future travel time and the current status travel time.The slope and in-tercept of this relationship may change subject to the time of day and the time until departure,but linearity persists.This observa-tion leads to a prediction scheme by means of linear regression with time-varying coefficients.Index Terms—Intelligent transportation systems(ITS),linear regression,prediction,travel time,varying coefficients.I.I NTRODUCTIONT HE PERFORMANCE measurement system(PeMS)[1] is a large-scale freeway data collection,storage,and analysis project.It involves the Electrical Engineering and Computer Science(EECS)and Statistics Departments and the Institute of Transportation Studies,University of California, Berkeley,in cooperation with the California Department of Transportation(CalTrans).PeMS’s goal is to facilitate traffic research and assist CalTrans by quantifying the performance of California’s eful information in various forms is to be distributed among traffic managers,planners,and engineers; freeway users;and researchers.In real time,PeMS obtains loop-detector data on flow(count)and occupancy at selected locations,aggregated over30-s intervals.For all of California, this amounts to2GB/d.In its raw form,this data is of little use. In this paper,we focus our attention on travel-time prediction between any two points on a freeway network for any future departure time.Besides being useful per se,travel-time predic-tion serves as an input or aid to dynamic route guidance[2], congestion management,and control[3];optimal routing and dispatching[4],[5];and incident detection[6].Manuscript received January4,2002;revised August16,2003and February 9,2004.The performance measurement system(PeMS)project is supported by grants from the National Science Foundation and from the California De-partment of Transportation(CalTrans)through the California PATH program. The Associate Editor for this paper was M.Papageorgiou.J.Rice is with the Department of Statistics,University of California,Berkeley, CA94720USA(e-mail:rice@).E.van Zwet is with the Mathematical Institute,University of Leiden,Leiden 2300RA,The Netherlands(e-mail:evanzwet@math.leidenuniv.nl).Digital Object Identifier10.1109/TITS.2004.833765We want our methods to be simple,fast,and scalable.We are currently developing an Internet application that will give the commuters of CalTrans District7(Los Angeles)the opportu-nity to query the prediction algorithm that is described in this paper.The user will access our Internet site and state the origin, destination,and time of departure(or desired time of arrival). He or she will then receive a prediction of the travel time and the best(fastest)route to take.To make useful predictions in a rapidly changing environment,it is clear that we need to be able to very quickly process very large amounts of plex al-gorithms will not do for our purpose.In Section II,we state the exact nature of our prediction problem.We then describe our new prediction method(“linear regression”)and two alternative methods that will be used for comparison.This comparison is made in Section III with a collection of34d of traffic data from a48-mi stretch of I-10 East in Los Angeles,CA.Finally,in Section IV,we summarize our conclusion,note some practical observations,and briefly discuss several extensions of our new method.II.M ETHODS OF P REDICTIONConsider anarray()denoting the velocity that was measured onday at loop at time.In Fig.3,we see an example of a velocity field for one day.From ,we can approximate thetime needed to travel from loop1toloop startingon at time.This travel time can be thought of as belonging to a path through the velocity field. It is important to note that in order to actuallycomputewe need information after ing information available at time,we can compute a proxy for the travel time definedas(1)where denotes the distance from loop toloop.Wecall the instantaneous or current status travel time.It is the travel time that would have resulted from the departure from loop1at time onday when no significant changes in traffic occurred untilloop was reached.If we havecomputed for acollection of days in the past,we then can also compute the average historical travel timeas(2) Our goal is topredictfor on the basis of the available information onday at time.Wecall the “time lag”and note that evenfor this problem is not trivial.We are certainly not the first to study this problem.1524-9050/04$20.00©2004IEEEFig.1.T (9AM )versus T (9AM ).Also shown is the regression line with intercept =17:3and slope =0:65.Fig.2.T (3PM )versus T (4PM ).Also shown is the regression line with intercept =9:5and slope =1:1.In the literature,we find spectral analysis [7],Kalman filtering [8],[9],linear models [10],[11],and autoregressive-integrated moving average (ARIMA)models [12],[13].Clustering techniques have been applied [10],[12]and,in recent years,also neural networks [14],[15].We refer to [15]for additional references.Many of these efforts are aimed at forecasting traffic flow rather than travel time.Also,several make use of automated vehicle identification (A VI),which is not available in many situations.Finally,and most importantly,our focus differs from the abovementioned papers.We do not aim for sophistication or statistical optimality,but for ease of imple-mentation and computational efficiency.We want our methodto be fully scalable to the very large amounts of data that we need to process very quickly to be able to advise motorists in real time.Two na ïve predictorsofare the instantaneous traveltimeand the historicalaverage .We expect —and,indeed,this is confirmed by an experiment —thatpredicts well forsmalland predicts betterfor large .We will improve on both these predictors for all .A.Linear RegressionThe main point of this paper is what appears to be an empir-ical fact:that there exist linear relationshipsbetweenFig.3.Velocity field v(d;l;t)where day d=June16;2000.Darker shades refer to lower speeds.Note that the typical triangular shapes indicate the morning and afternoon congestions building and easing.The horizontal streaks are most likely due to detector malfunction.and for all and.This observation has held upin all of numerous freeway segments in CA that we have ex-amined.This relation is illustrated by Figs.1and2,which arescatter plotsofversus for a48-mi stretch ofI-10East in Los Angeles.Note that the relation varies with thechoice of and.With this in mind,we propose themodel(3)where is a zero-mean random variable modeling random fluc-tuations and measurement errors.Note that theparametersandare allowed to vary with and.Linear models with varyingparameters are discussed by Hastie and Tibshirani[16].Fitting the model to our data is a familiar linear regressionproblem that we solve by weighted least squares.Define thepairand tominimize(4)where denotes the Gaussian density with mean zero and acertainvariance,which the user needs tospecify.(5)The purpose of this weight function is to impose smoothnessonand as functions of and.We assumethatand aresmooth in and,because we expect that average properties oftraffic do not change abruptly.The actual predictionofbecomes(6)Writing,we see that(3)expresses afuture travel time as a linear combination of the historical meanand the current status travel time—our two naïve predictors.Hence,our new predictor may be interpreted as the best linearcombination of our naïve predictors.From this point of view,wecan expect our predictor to do better than both.In fact,it does,as demonstrated in Section III.Another way to think about(3)is by remembering that theword“regression”arose from the phrase“regression to themean.”In our context,we would expect thatif ismuch larger than average,signifying severe congestion,thencongestion will probably ease during the course of the trip.Onthe other hand,if it is much smaller than average,congestion isunusually light and the situation will probably worsen duringthe journey.Besides comparing our predictor to the historical mean andthe current status travel time,we subject it to a more competitivetest.We consider two other predictors that may be expected to dowell,one resulting from principal component analysis and onefrom the nearest neighbors principle.Next,we describe thesetwo methods.B.Principal ComponentsOur predictor only uses information at one time point:thecurrent time onday.However,we do have information priorto that time.The following method attempts to exploit this byusing thetrajectoriesand forall.Let denote the vector of travel times for all departuretimes onday.Let us assume that the travel times on dif-ferent days are independently and identically distributed(i.i.d.)and that for a givenday,thevectorsand are mul-tivariate normal.We estimate the covariance of this multivariatenormal distribution by retaining only a few of the largest eigen-values in the singular value decomposition of the empirical co-varianceofand .The number of the eigenvalues re-tained must be specified by the user.Forgiven and ,define to be suchthat.Thatis,is the departure time of the last trip that was completed before time .With the estimated covariance,we can now com-pute the conditional expectationofgiven foralland forall .This is a standard computa-tion that is described;for instance,in [17].We call the resultingpredictor.C.Nearest NeighborsAs an alternative to principal components,we now considernearest neighbors,which also is an attempt to use information prior to the current time .Similar to principal components,it is a nonparametric method,but makes fewer assumptions (such asjoint normality)on the relationbetweenand .Nearest neighbors aims to find that day in the past that is most similar to the present day in some appropriate sense.The re-mainder of that past day beyond time is then taken as a pre-dictor of the remainder of the present day.The trick with nearest neighbors is in finding a suitable dis-tance between twodaysand .We suggest two possibledistances(7)and(8)Now,ifday minimizes the distanceto among all previousdays,our predictionis(9)Sensible modifications of the method are “windowed ”nearest neighbors (NN)and -NN.Windowed-NN recognizes that not all information prior to is equally relevant.Choosing a “window size”,it takes the above summation to rangeoverallbetweenand .So-called -NN basically is a smoothing method,aimed at using more information than is present in just the single closest match.For some valueof ,it findsthe closest daysin and bases a prediction on a (possibly weighted)combination of these.Alas,neither of thesevariants appear to significantly improve on the “vanilla”.III.R ESULTSWe have gathered flow and occupancy data from 116single-loop detectors along 48miles of I-10East in Los Angeles (between postmiles 1.28and 48.525).Measurements were done at 5-min aggregation at times ranging from 5AM to 9PM for 34weekdays between June 16and September 8,2000.We have used theso-called -factor method to convert flow and occupancy to velocity using the well-known formulavelocity flowoccupancy(10)Here,is the unknown average length of vehicles,which has to be estimated.Often,a fixed car length is assumed at all loops and all times and inserted into the formula.PeMS research,carried out by Jia et al.[18],indicates that this is far too na ïve.Jia et al.have studied data from 20double-loop detectors from Orange County,CA (CalTrans District 12)for a 3-mo period starting in January 1998.Double-loop detectors consist of two single loops in close proximity,allowing for direct measurement of velocity.Jia et al.conclude thatThe analysis shows thatthe -factor for any two ran-domly picked detectors in this network differ on average by 26percent,and with probability 0.12that they will differ by 50percent.Furthermore,the -factor for the same de-tector can vary by as much as 50percent over a 24-hour period.[18,p.536]The differences between two loops might be due to different calibration or sensitivity.It could also be due to the fact the ratio of trucks versus cars will differ at different locations.This ratio also changes during the day from mostly trucks at night to compacts during rush hour.This explains whythe -factor at the same loop varies with time.Jia et al.[18]propose a method that essentially works as fol-lows.If occupancy is below a certain threshold,say 10%,then it is assumed that the freeway is in a state of freeflow with a nom-inal speed of 60mi/h.Keeping the velocity fixed,(10)allows us to trackthe -factor.As soon as the occupancy threshold is ex-ceeded,a state of congestion is assumed andthe -factor is kept fixed at the last computed value.We again turn to (10),but this time to track velocity as we keepthe -factor fixed.The method appears to work well in practice.Another difficulty in computing the velocity field is that loop detectors often do not report correct values or do not report at all.Fortunately,the quality of our I-10data is quite good and we have used simple interpolation to impute incorrect or missingvalues.The resulting velocityfieldis shown in Fig.3,whereday is June 16.The horizontal streaks typically indicate detector malfunction.From the velocities,we computed travel times for tripsstarting between 5AM and 8PM .Fig.4showstheas functions of the time of day .Note the distinctive morning and afternoon congestions and the huge variability of travel times,especially during those periods.During afternoon rush hour,we find travel times of 45min to up to 2h.Included in the data are holidays July 3and 4,which may be readily recognized by their very fast travel times.We have estimated the root-mean-squared (rms)error of our various prediction methods for a number of “current times ”(AM ,AM PM )andlags(and 60min).We did the estimation by leaving out one day at a time,performing the prediction for that day on the basis of the other remaining days and averaging the squared prediction errors.The prediction methods all have parameters that must be specified by the user.For the regression method,we havechosen the standard deviation of the Gaussiankernelto be 10min.For the principal components method,we have chosen the number of eigenvalues retained to be 4.For the NN method,we have chosen distance function (8),awindow of 20min,and thenumber of nearest neighbors to be 2.Figs.5and 7show the estimated rms errorof,,and as predictorsof ,forlagFig.4.Travel times T (d;1)for 34d on a 48-mile stretch of I-10East.Fig.5.Estimated root-mean-square error (rmse),lag =0min.Historical mean (–1–),current status (–––),and linear regression (—).equal to 0and 60min,respectively.Note how the currentstatusperforms well forsmall()and how the his-toricalmeandoes not become worseas increases.Most importantly,however,notice how the regressionpredictorbeats both uniformly.Figs.6and 8again show the rms prediction error of the regression predictor.This time,its performance is comparedto the principal componentspredictor and the NNpredictor .Again,the regression predictorcomes out on top,although the NN predictor shows comparable performance.The rms error of the regression predictor stays below 10min even when predicting 1h ahead.As the average travel time is multiplied by 3,the rms error is still less than 10%.We feel that this is particularly impressive given the simplicity of the predictor.Fig.6.Estimated rmse,lag =0min.Principal components (–1–),nearest neighbors (–––),and linear regression (—).Fig.7.Estimated rmse,lag =60min.Historical mean (–1–),current status (–––),and linear regression (—).IV .C ONCLUSION AND L OOSE E NDSThe main contribution of this paper is the discovery of a linearrelationbetweenand ,which we put to use to predict travel parison of the regression pre-dictor to the principal components and NN predictors yieldsanother surprise.Given,there is not much informa-tion left in theearlier()that is useful for pre-dicting .In fact,we have come to believe that for the purpose of predicting travel times all the informationinis well summarized by one singlenumber:.It is of practical importance to note that our prediction can beperformed in real putation of theparametersand is time consuming,but can be done offline in reasonable time.The actual prediction is trivial.As this paper was submitted,we were in the process of making our travel-time predictions and associated optimal routings available through the Internet for the network of freeways of California District 7(Los Angeles).It would also be possible to make our service available for users of cellular telephones —in fact,we plan to do so in the near future.It also is important to notice that our method does not rely on any particular form of data.In this paper,we have used single-loop detectors,but probe vehicles or video data can beFig.8.Estimated rmse,lag=60min.Principal components(–1–),NN(–––),and linear regression(—). used in place of loops,since all the method requires is the cur-rent valueof and historical measurementsofand.Earlier work with the method[19]used a short stretch offreeway with very-high-quality data,including probe vehicles.In that work,however,the regression method was not comparedto methods such as NN and principal components,which takemore information into account.We conclude this paper by briefly noting two extensions ofour prediction method.1)For tripsfromtoto,wehave(11)We have found that it is sometimes more practical or ad-vantageous to predict the terms on the right-hand sidethan topredict directly.For instance,when pre-dicting travel times across networks(graphs),we needonly predict travel times for the edges and then use(11)to piece these together to obtain predictions for arbitraryroutes.2)We regressed the traveltime on the currentstatus.Now,define to be the traveltime for a trip arriving attime onday.Regressingon will allow us to make predictionson the travel time subject to arrival attime.The usercan thus ask what time he or she should depart in order toreach his or her intended destination at a desired time.A CKNOWLEDGMENTThe authors would like to acknowledge the help(alphabeti-cally)from P.Bickel,C.Chen,J.Kwon,X.Zhang,and the othermembers of the PeMS project.Also,we would like to thank theengineers of CalTrans Headquarters and Districts7and12.R EFERENCES[1][Online].Available:/Public/[2]H.S.Mahmassani and S.Peeta,“System optimal dynamic assignmentfor electronic route guidance in a congested traffic network,”in UrbanTraffic Networks:Dynamic Flow Modeling and Control,Gartner andImprota,Eds.Berlin,Germany:Springer-Verlag,1995,pp.2–27.[3] F.Ho and P.Ioannou,“Traffic flow modeling and control using artificialneural networks,”IEEE Control Syst.Mag.,vol.16,pp.16–27,Oct.1996.[4]I.Chabini,“Discrete dynamic shortest path problems in transportationapplications:Complexity and algorithms with optimal run time,”Trans-port.Res.Rec.,vol.1645,pp.170–175,1998.[5] A.Orda and R.Rom,“Minimum weight paths in time-dependent net-works,”Networks,vol.21,no.3,pp.295–320,1991.[6]Y.J.Stephanedes and A.P.Chassiakos,“Freeway incident detectionthrough filtering,”Transport.Res.C,vol.1,no.3,pp.219–233,1993.[7]H.Nicholson and C.Swann,“The prediction of traffic flow volumesbased on spectral analysis,”Transport.Res.,vol.8,pp.533–538,1974.[8]I.Okutani and Y.Stephanedes,“Dynamic prediction of traffic volumethrough Kalman filtering theory,”Transport.Res.B,vol.18,no.1,pp.1–11,1984.[9]P.C.Vythoulkas,“Alternative approaches to short term traffic fore-casting for use in driver information systems,”in Proc.Int.Symp.Trans-portation Traffic Theory,1993,pp.485–506.[10]M.Danech-Pajouh and M.Aron,“ATHENA,a Method for Short-TermInter-Urban Traffic Forecasting,”INRETS,Paris,France,Rep.177,1991.[11]J.Kwon,B.Coifman,and P.Bickel,Day-to-day travel time trendsand travel time prediction from loop detector data,in Transport.Res.Rec.1717,Transportation Research Board,pp.120–129,2000.[12]M.Van der V oort,M.Dougherty,and S.Watson,“Combining KO-HONEN maps with ARIMA time series models to forecast trafficflow,”Transport.Res.C,vol.4,no.5,pp.307–318,1996.[13]T.Oda,“An algorithm for prediction of travel time using vehicle sensordata,”in Proc.3rd Int.Conf.Road Traffic Control,1990,pp.40–44.[14] D.Park and L.Rilett,“Multiple-period freeway link travel times using-modular neural networks,”Transport.Res.Rec.,vol.1617,pp.163–170,1998.[15]H.R.Kirby,S.M.Watson,and M.S.Dougherty,“Should we useneural networks or statistical models for short-term motorway trafficforecasting?,”Proc.Int.J.Forecasting,vol.13,no.1,pp.43–50,1997.[16]T.Hastie and R.Tibshirani,“Varying coefficient models,”J.R.Stat.Soc.,ser.B,vol.55,no.4,pp.757–796,1993.[17]K.V.Mardia,J.T.Kent,and S.M.Bibby,Multivariate Anal-ysis.London,U.K.:Academic,1979.[18]Z.Jia,C.Chen,B.Coifman,and P.Varaiya,“The PeMS algorithms foraccurate,real-time estimates of g-factors and speeds from single loop detectors,”in Proc.4th Int.ITSC Conf.,2001,pp.536–541.[19]X.Zhang and J.Rice,“Short term travel time prediction,”Transport.Res.,ser.C,vol.11,pp.187–210,2003.John Rice was born in New York on June14,1944.He received the B.A.degree in mathematics from the University of North Carolina,Chapel Hill,in1966and the Ph.D.degree in statistics from the University of California,Berkeley,in 1972.He was with the Department of Mathematics,University of California, San Diego,from1973to1991.Since1991,he has been with the University of California,Berkeley,where he currently is Professor of statistics.His research interests include applied and theoretical statistics.Dr.Rice is a Member of the Institute of Mathematical Statistics,the American Statistical Association,and the International StatisticalInstitute.Erik van Zwet was born in The Hague,The Netherlands,on November10,1970.He received the doctorandus(M.Sc.)degree from the University of Leiden,Leiden,the Netherlands,in1995and the Ph.D.degree from the University of Utrecht,Utrecht, The Netherlands,in1999,both in mathematics. After three years as a post-doctorate with the Uni-versity of California,Berkeley,he is now an Assistant Professor with the University of Leiden.。

Aviation Weather Handbook2022United States Department of TransportationFEDERAL AVIATION ADMINISTRATIONFlight Standards ServicePrefaceThis handbook consolidates the weather information from the following advisory circulars (AC) into one source document. By doing this, the Federal Aviation Administration (FAA) intends to streamline access to the FAA’s weather documentation for users of the National Airspace System (NAS). The following ACs will remain in effect, but they will eventually be cancelled at a later date following the publication of this handbook:•AC 00-6, Aviation Weather.•AC 00-24, Thunderstorms.•AC 00-30, Clear Air Turbulence Avoidance.•AC 00-45, Aviation Weather Services.•AC 00-54, Pilot Windshear Guide.•AC 00-57, Hazardous Mountain Winds.This handbook is designed as a technical reference for all who operate in the NAS. Pilots, dispatchers, and operators will find this handbook a valuable resource for flight planning and decision making.This handbook conforms to pilot weather training and certification concepts established by the FAA. The discussion and explanations reflect the most commonly used weather products and information.It is essential for persons using this handbook to also become familiar with and apply the pertinent parts of Title 14 of the Code of Federal Regulations (14 CFR) and the Aeronautical Information Manual (AIM). Title 14 CFR, the AIM, this handbook, current ACs, and other FAA technical references are available via the internet at the FAA home page https://.This handbook is available for download in Portable Document Format (PDF) from the FAA’s Regulations and Policies web page at https:///regulations_policies/handbooks_manuals/aviation/.This handbook is published by the U.S. Department of Transportation (DOT), FAA Flight Technologies and Procedures Division.The guidance and recommendations in this handbook are not legally binding in their own right and will not be relied upon by the FAA as a separate basis for affirmative enforcement action or other administrative penalty. Conformity with the guidance and recommendations is voluntary only and nonconformity will not affect rights and obligations under existing statutes and regulations.Comments regarding this publication should be sent, in email form, to the following address: **************************.Wesley L. MootyActing Deputy Executive Director, Flight Standards Service11/25/2022AcknowledgementsThis document was prepared under FAA Contract DTFAWA-15-D-00031 with support from AvMet Applications, Inc.Individual photographic contributions were made by Blake Spry (front cover), Luke Culver (front cover and cover of Chapter 15), and Larry Burch (covers of Chapters 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 16, 18, 23, 24, 25, 26, 27, 28, Part 1, Part 2 and Part 3). Cover of Chapter 20 courtesy of the National Research Council of Canada.NoticeThe U.S. Government does not endorse products or manufacturers. Trade or manufacturers’ names appear herein solely because they are considered essential to the objective of this handbook.Table of ContentsPreface .................................................................................................................................................. i ii Acknowledgements ................................................................................................................................ i v Notice (v)Table of Contents ................................................................................................................................... v i Chapter 1. Introduction ......................................................................................................................... 1-1 Part 1: Overview of the United States Aviation Weather Service Program and InformationChapter 2. Aviation Weather Service Program ...................................................................................... 2-1 2.1 Introduction .......................................................................................................................... 2-1 2.2 National Oceanic and Atmospheric Administration (NOAA) ................................................ 2-22.2.1 National Environmental Satellite, Data, and Information Service (NESDIS) .................. 2-22.2.2 National Weather Service (NWS) .................................................................................. 2-2 2.3 Federal Aviation Administration (FAA) ................................................................................ 2-42.3.1 Air Traffic Control Systems Command Center (ATCSCC) ............................................ 2-42.3.2 Air Route Traffic Control Center (ARTCC) ................................................................... 2-52.3.3 Airport Traffic Control Tower (ATCT) and Terminal Radar ApproachControl (TRACON) ...................................................................................................... 2-52.3.4 Flight Service ................................................................................................................ 2-5 2.4 Department of Defense (DOD) ............................................................................................. 2-5 2.5 Commercial Weather Information Providers ......................................................................... 2-5 Chapter 3. Overview of Aviation Weather Information ......................................................................... 3-1 3.1 Introduction .......................................................................................................................... 3-1 3.2 Use of Aviation Weather Information ................................................................................... 3-23.2.1 Product Latency ............................................................................................................ 3-23.2.2 Additional Use Information ........................................................................................... 3-2 3.3 Obtaining Weather Information............................................................................................. 3-23.3.1 Weather Briefings.......................................................................................................... 3-23.3.2 Telephone...................................................................................................................... 3-43.3.3 Self-Briefing ................................................................................................................. 3-53.3.4 In-Flight Updates........................................................................................................... 3-6 3.4 Overview of Aviation Weather Products ............................................................................... 3-73.4.1 Weather Products versus Weather Elements and Phenomena ......................................... 3-73.4.2 Summaries of Specific Weather Information Contained in Various WeatherProducts ...................................................................................................................... 3-13Part 2: Weather Theory and Aviation HazardsChapter 4. The Earth’s Atmosphere ...................................................................................................... 4-1 4.1 Introduction .......................................................................................................................... 4-1 4.2 Composition ......................................................................................................................... 4-24.2.1 Air Parcel ...................................................................................................................... 4-3 4.3 Vertical Structure .................................................................................................................. 4-34.3.1 Troposphere .................................................................................................................. 4-34.3.2 Stratosphere................................................................................................................... 4-34.3.3 Mesosphere ................................................................................................................... 4-44.3.4 Thermosphere................................................................................................................ 4-44.3.5 Exosphere...................................................................................................................... 4-4 4.4 The Standard Atmosphere ..................................................................................................... 4-5 Chapter5. Heat and Temperature.......................................................................................................... 5-1 5.1 Introduction .......................................................................................................................... 5-1 5.2 Matter ................................................................................................................................... 5-2 5.3 Energy .................................................................................................................................. 5-2 5.4 Heat ...................................................................................................................................... 5-2 5.5 Temperature ......................................................................................................................... 5-25.5.1 Temperature Measurement ............................................................................................ 5-25.5.2 Temperature Scales ....................................................................................................... 5-2 5.6 Heat Transfer ........................................................................................................................ 5-45.6.1 Radiation ....................................................................................................................... 5-45.6.2 Conduction .................................................................................................................... 5-65.6.3 Convection .................................................................................................................... 5-7 5.7 Thermal Response ................................................................................................................ 5-8 5.8 Temperature Variations with Altitude ................................................................................. 5-115.8.1 Atmospheric Sounding ................................................................................................ 5-115.8.2 Isothermal Layer ......................................................................................................... 5-115.8.3 Temperature Inversion ................................................................................................. 5-12 Chapter6. Water Vapor ........................................................................................................................ 6-1 6.1 Introduction .......................................................................................................................... 6-1 6.2 The Hydrologic Cycle ........................................................................................................... 6-26.2.1 Evaporation ................................................................................................................... 6-26.2.2 Transpiration ................................................................................................................. 6-26.2.3 Sublimation ................................................................................................................... 6-26.2.4 Deposition ..................................................................................................................... 6-36.2.5 Condensation ................................................................................................................. 6-36.2.6 Transportation ............................................................................................................... 6-36.2.7 Precipitation .................................................................................................................. 6-36.2.8 Runoff ........................................................................................................................... 6-36.2.9 Infiltration ..................................................................................................................... 6-36.2.10 Groundwater Flow ......................................................................................................... 6-36.2.11 Plant Uptake .................................................................................................................. 6-3 6.3 Saturation ............................................................................................................................. 6-3 6.4 Relative Humidity................................................................................................................. 6-4 6.5 Dewpoint .............................................................................................................................. 6-4 6.6 Temperature-Dewpoint Spread (Dewpoint Depression) ......................................................... 6-4 6.7 Change of Phase ................................................................................................................... 6-56.7.1 Latent Heat .................................................................................................................... 6-6 Chapter 7. Earth-Atmosphere Heat Imbalances ..................................................................................... 7-1 7.1 Introduction .......................................................................................................................... 7-1 7.2 The Earth-Atmosphere Energy Balance................................................................................. 7-2 7.3 Heat Imbalances Between Earth’s Surface and the Atmosphere ............................................. 7-37.3.1 Sensible Heating ............................................................................................................ 7-37.3.2 Latent Heat .................................................................................................................... 7-4 7.4 Heat Imbalance Variations with Latitude............................................................................... 7-5 7.5 Seasons................................................................................................................................. 7-6 7.6 Diurnal Temperature Variation ............................................................................................. 7-7 Chapter 8. Atmospheric Pressure and Altimetry .................................................................................... 8-1 8.1 Introduction .......................................................................................................................... 8-1 8.2 Atmospheric Pressure ........................................................................................................... 8-28.2.1 Barometer...................................................................................................................... 8-28.2.2 Atmospheric Pressure Units ........................................................................................... 8-38.2.3 Station Pressure ............................................................................................................. 8-38.2.4 Pressure Variation ......................................................................................................... 8-48.2.5 Sea Level Pressure......................................................................................................... 8-6 8.3 Density ................................................................................................................................. 8-68.3.1 Volume’s Effects on Density ......................................................................................... 8-78.3.2 Changes in Density ........................................................................................................ 8-88.3.3 Density’s Effects on Pressure......................................................................................... 8-88.3.4 Temperature’s Effects on Density .................................................................................. 8-98.3.5 Water Vapor’s Effects on Density .................................................................................. 8-9 8.4 Altimetry ............................................................................................................................ 8-108.4.1 Altitude ....................................................................................................................... 8-10 Chapter 9. Global Circulations and Jet Streams ..................................................................................... 9-1 9.1 Introduction .......................................................................................................................... 9-1 9.2 Non-Rotating Earth Circulation System ................................................................................ 9-2 9.3 Rotating Earth Circulation System ........................................................................................ 9-2 9.4 Jet Streams ........................................................................................................................... 9-39.4.1 Direction of Wind Flow ................................................................................................. 9-39.4.2 Location ........................................................................................................................ 9-4 Chapter 10. Wind ............................................................................................................................... 10-1 10.1 Introduction ........................................................................................................................ 10-1 10.2 Naming of the Wind ........................................................................................................... 10-2 10.3 Forces That Affect the Wind ............................................................................................... 10-210.3.1 Pressure Gradient Force (PGF) .................................................................................... 10-210.3.2 Coriolis Force .............................................................................................................. 10-310.3.3 Friction Force .............................................................................................................. 10-5 10.4 Upper Air Wind .................................................................................................................. 10-6 10.5 Surface Wind ...................................................................................................................... 10-7 10.6 Local Winds ....................................................................................................................... 10-810.6.1 Sea Breeze................................................................................................................... 10-910.6.2 Land Breeze .............................................................................................................. 10-1110.6.3 Lake Breeze .............................................................................................................. 10-1210.6.4 Valley Breeze ............................................................................................................ 10-1410.6.5 Mountain-Plains Wind System................................................................................... 10-1410.6.6 Mountain Breeze ....................................................................................................... 10-15 10.7 Adverse Winds ................................................................................................................. 10-1610.7.1 Crosswind ................................................................................................................. 10-1610.7.2 Gust .......................................................................................................................... 10-1710.7.3 Tailwind .................................................................................................................... 10-1710.7.4 Variable Wind/Sudden Wind Shift ............................................................................. 10-1810.7.5 Wind Shear ............................................................................................................... 10-1810.7.6 Adverse Mountain Winds .......................................................................................... 10-1810.7.7 Atmospheric Disturbances in Mountainous Areas ...................................................... 10-18Chapter 11. Air Masses, Fronts, and the Wave Cyclone Model ........................................................... 11-1 11.1 Introduction ........................................................................................................................ 11-1 11.2 Air Masses .......................................................................................................................... 11-211.2.1 Air Mass Classification................................................................................................ 11-211.2.2 Air Mass Modification ................................................................................................. 11-3 11.3 Fronts ................................................................................................................................. 11-411.3.1 Warm Front ................................................................................................................. 11-511.3.2 Cold Front ................................................................................................................... 11-611.3.3 Stationary Front ........................................................................................................... 11-711.3.4 Occluded Front ............................................................................................................ 11-8 11.4 The Wave Cyclone Model .................................................................................................. 11-9 11.5 Dryline ............................................................................................................................. 11-11 Chapter 12. Vertical Motion and Clouds ............................................................................................. 12-1 12.1 Introduction ........................................................................................................................ 12-1 12.2 Vertical Motion Effects on an Unsaturated Air Parcel ......................................................... 12-2 12.3 Vertical Motion Effects on a Saturated Air Parcel ............................................................... 12-3 12.4 Common Sources of Vertical Motion .................................................................................. 12-512.4.1 Orographic Effects ...................................................................................................... 12-512.4.2 Frictional Effects ......................................................................................................... 12-612.4.3 Frontal Lift .................................................................................................................. 12-712.4.4 Buoyancy .................................................................................................................... 12-7 12.5 Cloud Forms ....................................................................................................................... 12-8 12.6 Cloud Levels ...................................................................................................................... 12-8 Chapter 13. Atmospheric Stability ...................................................................................................... 13-1 13.1 Introduction ........................................................................................................................ 13-1 13.2 Using a Parcel as a Tool to Evaluate Stability...................................................................... 13-2 13.3 Stability Types .................................................................................................................... 13-213.3.1 Absolute Stability ........................................................................................................ 13-213.3.2 Neutral Stability .......................................................................................................... 13-313.3.3 Absolute Instability ..................................................................................................... 13-413.3.4 Conditional Instability ................................................................................................. 13-513.3.5 Summary of Stability Types ........................................................................................ 13-6 13.4 Processes That Change Atmospheric Stability ..................................................................... 13-813.4.1 Wind Effects on Stability ............................................................................................. 13-813.4.2 Vertical Air Motion Effects on Stability ....................................................................... 13-813.4.3 Diurnal Temperature Variation Effects on Stability ...................................................... 13-9 13.5 Measurements of Stability................................................................................................. 13-1013.5.1 Lifted Index (LI)........................................................................................................ 13-1013.5.2 Convective Available Potential Energy (CAPE) ......................................................... 13-11 13.6 Summary .......................................................................................................................... 13-11 Chapter 14. Precipitation .................................................................................................................... 14-1 14.1 Introduction ........................................................................................................................ 14-1 14.2 Necessary Ingredients for Formation ................................................................................... 14-2 14.3 Growth Process ................................................................................................................... 14-2 14.4 Precipitation Types ............................................................................................................. 14-314.4.1 Snow ........................................................................................................................... 14-314.4.2 Ice Pellets .................................................................................................................... 14-414.4.3 Freezing Rain .............................................................................................................. 14-414.4.4 Rain ............................................................................................................................ 14-514.4.5 Hail ............................................................................................................................. 14-5 Chapter 15. Weather Radar ................................................................................................................. 15-1 15.1 Introduction ........................................................................................................................ 15-1 15.2 Principles of Weather Radar................................................................................................ 15-215.2.1 Antenna ....................................................................................................................... 15-215.2.2 Backscattered Energy .................................................................................................. 15-215.2.3 Power Output .............................................................................................................. 15-315.2.4 Wavelengths ................................................................................................................ 15-315.2.5 Attenuation .................................................................................................................. 15-415.2.6 Resolution ................................................................................................................... 15-515.2.7 Wave Propagation ....................................................................................................... 15-715.2.8 Radar Beam Overshooting and Undershooting ........................................................... 15-1015.2.9 Beam Blockage ......................................................................................................... 15-1115.2.10 Ground Clutter .......................................................................................................... 15-1115.2.11 Ghost......................................................................................................................... 15-1215.2.12 Angels ....................................................................................................................... 15-1315.2.13 Anomalous Propagation (AP) .................................................................................... 15-1415.2.14 Other Nonmeteorological Phenomena ........................................................................ 15-1515.2.15 Precipitation .............................................................................................................. 15-16 Chapter 16. Mountain Weather ........................................................................................................... 16-1 16.1 Introduction ........................................................................................................................ 16-1。

我们如何解决交通问题英语作文初一全文共5篇示例，供读者参考篇1How Can We Solve Traffic Problems?Hi everyone! I'm going to talk about a really big problem that affects a lot of people - traffic! Have you ever been stuck in a big traffic jam and felt like you were never going to get wherever you were going? I have, and it's the worst! Cars are honking, people are getting frustrated, and time is just ticking away. What a headache!Traffic is caused when there are too many cars on the road at the same time. With so many vehicles trying to get somewhere, the roads can't handle it all and everything slows way down or even stops completely. Major cities have the biggest traffic issues because that's where lots of people live and work. During rush hours in the morning and evening when everyone is trying to get to school or work and then get back home, the roads are super crowded!But traffic isn't just an annoyance, it actually causes a lot of other problems too. First of all, it's really bad for the environment.When cars are stuck not moving, they are still releasing exhaust fumes and polluting the air we breathe. All those idling vehicles are wasting a ton of gas too, which isn't good for anyone's wallet. Traffic jams make people late for things like work, appointments, or picking up their kids. That can get people in trouble or make them miss out on stuff. And sitting in traffic is also really stressful for drivers!So what can we do about this big traffic headache? Well, there are actually quite a few solutions that can help reduce traffic congestion. One thing we can do is have fewer cars on the roads, period. More people could take public transportation like buses, trains, or subways instead of driving themselves. That would mean way fewer vehicles out there causing backups. We can also encourage behaviors like carpooling where people share rides instead of everyone driving alone.Another solution is to improve the roads and transportation systems themselves. Building better roads with more lanes, optimized traffic light patterns, and making repairs can allow traffic to flow more smoothly. Cities can invest in expanding public transit like buses and trains so it's easier for people to use those options instead of their own cars. And improvingpedestrian accessibility with things like bike lanes and walkable paths means some journeys don't even require a vehicle at all!Technology can also help us tackle the traffic problem in really cool ways. Smart traffic light systems can sense when congestion is building up in certain areas and adjust light patterns to prevent gridlock. GPS apps can suggest alternative routes when roads are jammed. Self-driving cars that can communicate with each other and drive more efficiently could significantly reduce start-and-stop traffic waves that make congestion worse.But we also need to change some of our habits and mindsets too. Maybe we don't all need to be rushing onto the roads at the exact same times for work or school, causing those peak rush hours. Companies could stagger work schedules or embrace more work-from-home flexibility so not everyone is trying to commute at once. And when possible, people could walk, bike, or take transit for shorter local trips instead of driving.Solving traffic will take a lot of teamwork from government leaders, city planners, technology companies, and all of us everyday people too. By driving less, exploring alternative transportation, being smarter about our travel habits, and improving aging infrastructure, we can get traffic moving again.It will take time and effort, but getting traffic under control will benefit so many people. Less pollution, money savings, reduced stress, and more free time to do the things we actually want to do instead of just sitting in traffic! What a dream that would be.So those are some of my thoughts on how we can work to solve the traffic problem. It's a huge challenge for sure, but I think by working together we can make it happen. Let's get those roads cleared up so we can all stop going nowhere fast and get where we need to go efficiently! What kinds oftraffic-beating solutions would you suggest?篇2How Can We Solve Traffic Problems?Traffic is a really big problem in many cities around the world. It causes so many issues like pollution, wasted time, and stressful driving conditions. As kids, we might not be able to drive yet, but we are still affected by bad traffic. We breathe in the fumes from all the cars, and our parents have to spend a lot of time stuck in jams when they could be home playing with us instead.I think it's important that we try to solve these traffic problems when we grow up. The world can't just keep getting more and more congested on the roads. We need to find ways toreduce how much people drive and have more people using public transportation, riding bicycles, or even just walking when possible.One thing that could really help is if cities built better public transportation systems. My dad has to drive to work every day because there aren't any good bus or train routes near our house. But if there were, he could just hop on the bus or train instead of sitting in traffic. Public transportation is way better for the environment too since there's just one vehicle instead of everybody driving their own car and spewing out exhaust.We should also make cities more walkable and bikeable. A lot of neighborhoods are designed for cars but not really for people walking or biking around. There should be more sidewalks, crosswalks, and bicycle lanes so it's safer and easier for people to get around without a car for short trips. My friend Sam can't even bike to my house because there's no good route that doesn't require going onto a major road. That's no good!Another thing that could reduce traffic is if more people worked from home. With today's technology like video calls and cloud stuff, a lot of office jobs can actually be done from home pretty easily. My aunt works from home a few days per week and she loves not having to commute. If more companies let peoplework remotely some days, there would be way fewer cars on the road during rush hour. Pretty cool, right?Self-driving cars might also help one day. The self-driving cars could communicate with each other and drive more efficiently instead of everybody hitting the brakes constantly like human drivers do. Maybe the self-driving cars could even bypass traffic jams by taking side roads or something. I'm not totally sure how they would work, but self-driving vehicles seem way smarter than regular cars.Honestly though, I think one of the biggest solutions is if people just drove less in general. We shouldn't use a huge gas guzzling car to simply drive a few blocks away. If it's a short distance, we should try walking or biking instead of driving. It's way better exercise and doesn't make any pollution at all. My grandma always walks a few blocks to the grocery store instead of driving, and she's so fit because of it! My family also tries to do one outing that combines multiple errands rather than making separate driving trips. It's easy changes like this that can really make a difference.In the end, fixing traffic problems will require a whole bunch of changes by cities and also by individuals. We need better public transit, safer routes for biking and walking, more workfrom home opportunities, and smarter self-driving cars. But we should also change our own habits and drive less when possible. If everybody did their part, just imagine how much better traffic would be! We could actually get places without being stuck going 5 mph on the highway. What a dream! Solving traffic jams is something we should all care about for a cleaner environment and a less stressful world. Let's make it happen, people!篇3How Can We Solve Traffic Problems?Hi friends! Today I want to talk about a really big problem that affects all of us - traffic! Whether you walk, take the bus, or your parents drive you places, I'm sure you've been stuck in terrible traffic jams before. It's no fun at all! The roads are so crowded with cars that sometimes you can barely move. Engines are running but nobody is going anywhere fast. Exhaust fumes make the air super smoggy and hard to breathe. People are honking their horns and getting frustrated. What a mess!Traffic is bad for the environment too. All those cars and trucks spewing out gross exhaust is polluting the air we breathe. It's also really noisy with all the honking and engine sounds. Things like factories and power plants cause pollution as well,but vehicles are one of the biggest sources. The more traffic there is, the more pollution gets created. Yuck!So why is traffic such a big problem these days? Well, as cities have grown bigger and more people have moved to urban areas, the number of vehicles on the roads has increased a ton. Decades ago, not as many people owned cars. But nowadays, it seems like every family has one, two, or even more cars! All those extra vehicles are clogging up the roads and highways.Another reason for bad traffic is that the road systems in many cities haven't kept up with the growth in population and vehicles. There just aren't enough roads, bridges, tunnels and highways to handle the crazy number of cars trying to use them at the same time, especially during rush hours when everyone is trying to get to work or school. No wonder there are so many traffic snarls!Public transportation systems like buses, subways and trains also need improvement in some cities. If public transit was faster, cheaper and more convenient, more people might use it instead of driving themselves. That could reduce the number of private vehicles on the roads.So what can we do to help solve these traffic headaches? I have some ideas! First, we could try to drive less by carpooling,walking, riding bikes or taking public transportation when possible. If each family used just one car instead of two or three, that would be a huge help. We could even try giving up our cars completely and just use public transit, bikes or our own two feet to get around!City planners could also build more roads, bridges and transit systems to handle more vehicles. Widening main roads and highways with extra lanes would allow more cars to use them. Improving public transportation with better bus systems, more subway and train lines, and bike lanes would give people good alternatives to driving too.Another solution is developing "smarter" roads and traffic light systems that use sensors and computers to monitor traffic flow and adjust signal timings to keep vehicles moving smoothly. Traffic cops could also enforce traffic laws better to prevent drivers from blocking intersections or doing other things that make jams worse.In the longer term, we need to find cleaner transportation options besides gas-powered vehicles. Electric cars, trucks and buses don't spew out dirty exhaust like regular cars do. Using more renewable energy sources like solar, wind and hydro power plants instead of coal and oil could also reduce air pollution fromenergy production. Wouldn't it be cool if all vehicles were environmentally-friendly someday?We could also encourage people to live closer to where they work, go to school, shop and do other activities. That would mean less time spent driving long distances every day. Better urban planning could create "walkable" communities where most places are within a 10-15 minute walk of people's homes. How convenient would that be?Lastly, we need to explore options like working from home, online shopping, distance learning and videoconferencing to cut down on commuting. The less we have to be in transit during peak travel times, the less traffic there will be on the roads. Easy peasy!I know solving traffic might seem like a huge challenge with no simple solution. But by trying a combination of the ideas I mentioned, I think we could really make a difference! Installing smarter roads, improving public transit, finding cleaner transportation, better urban planning, driving less, and working from home could go a long way.It will take cooperation from regular people like you and me along with city leaders, transportation experts, lawmakers, businesses and others working together. But I'm an optimist andI believe we can do it! A future with way less traffic congestion, pollution and headaches is possible if we all pitch in. Let's get started today to create safer, cleaner and less stressful roads for tomorrow! Thank you!篇4How Can We Solve Traffic Problems?Traffic is such a big problem these days! Cars are everywhere and the roads are always so jammed up. It makes me late for school sometimes when my mom is stuck in a huge traffic jam. Pollution from all the cars is bad for the environment too. I really wish there was a way to solve these traffic problems!I think one of the main reasons for so much traffic is that too many people drive their own cars. If more people took the bus, subway, or trained instead, there would be way fewer cars clogging up the roads. My dad has to drive to work, but my mom could probably take the bus since her office is right on the bus route. I've been trying to convince her to do that so there's one less car on the highways.Another big reason for traffic is that people live really far away from where they work or go to school. That means they have to drive long distances on highways to get there. Ifcommunities had a better mix of housing areas close to offices, stores, and schools, people wouldn't have to travel as far. My best friend lives right across the street from our elementary school, so his mom just walks him there instead of driving. That's so convenient!Speaking of schools, I think it causes a ton of extra traffic when parents all drop off and pick up their kids by car. At my school, there's a huge line of cars waiting to drop kids off in the morning, and then it happens all over again at pickup time in the afternoon. All those cars definitely make the roads around school a mess. More kids should walk, bike, or take the school bus to reduce the traffic nightmares around schools.Construction and road work is another major headache that snarls up traffic. I remember one summer there was a huge construction project happening on the highway near my grandma's house. Every time we drove to visit her, we had to sit in a massive traffic jam going past the construction zone. Things would flow so much better if they could do road work at night when there are fewer cars out.Accidents are yet another thing that brings traffic to a stand-still. Even a minor fender bender can back up traffic for miles if it blocks part of the road. My dad says drivers need to bemore careful and patient to avoid accidents that cause gridlock. And when there is an accident, the police should work faster to get the wreckage cleared away so traffic can get moving again.You know what else is annoying? Poorly timed stoplights!I've sat at so many intersections waiting forever for the light to change, even when there are no other cars coming the other way. Smarter stoplights that adjust to traffic levels would keep cars moving instead of sitting idle. The city could even have trafficologists study the busiest intersections and reset the light patterns to allow better traffic flow.Self-driving cars are supposed to be the wave of the future for reducing traffic, according to the older kids at school. The idea is that computer-controlled cars can drive closer together and react faster than human drivers. That would allow more cars to fit on the roads without crashing into each other. Whileself-driving cars sound really futuristic and cool, I'm not sure I'd want to ride in one without a human driver!Anyway, those are some of my ideas for how we can try to solve traffic congestion. We need to encourage more people to take trains, buses, and public transit instead of driving individual cars. People should try to live closer to where they need to go instead of way out in the suburbs. Schools should have stricterpolicies so fewer parents can do drop-off and pick-up by car. Roads and highways need smarter construction schedules and faster accident cleanups. Intersections should have stoplights reprogrammed based on traffic patterns.If all those things could happen, imagine how much better traffic would be! No more sitting in huge jams breathing in sooooo much exhaust from other cars. We could zip around quickly to get wherever we need to go. Awesome!Of course, I know it's pretty unlikely that all of those solutions will actually happen anytime soon. Traffic problems are just so complicated with lots of different causes. But I really hope that when I'm a grown-up, much smarter people than me will have figured out ways to reduce traffic congestion. Sitting in traffic is definitely one of the most frustrating things in the world!篇5How Can We Solve Traffic Problems?Traffic is a huge problem in many cities around the world. Every day, there are sooooo many cars on the roads causing terrible traffic jams. It makes people late for school or work, and it's really bad for the environment too! As kids, we need to thinkabout solutions to this big traffic headache so we can have a better future.The biggest cause of traffic is that there are just too many cars on the roads. Seems like every family has multiple cars these days. Moms and dads drive separate cars to work, and even some teenagers have their own cars too! All those cars clogging up the streets is a nightmare. We need to find ways to reduce the number of cars.One idea is to encourage more people to use public transportation like buses, trains, and subways. Public transit is much better for the environment than driving a personal car. It reduces pollution and greenhouse gases that harm our planet. The problem is that in many cities, the public transit systems are not very good. The buses and trains don't run frequently enough, the routes don't go everywhere, and the vehicles are often crowded and uncomfortable. To get more people to ride public transit, cities need to invest a lot of money to improve and expand their systems.Another way to cut down on cars is to make it easier and safer for people to walk, bike, or use other "human-powered" transportation modes. Cities could build more sidewalks, crosswalks, pedestrian signals, and bike lanes to protect peoplefrom car traffic. They could also have "No Car" zones in downtown areas where only walkers, bikers, and public transit is allowed. Some cities have programs that let people rent bicycles, scooters, or electric bikes to get around without a car. The more cities do stuff like this, the less we will need so many cars choking the streets.Even with fewer overall cars, cities will still need to do things to keep the remaining traffic moving smoothly and safely. One of the biggest causes of traffic backups is car accidents and collisions. Stricter enforcement of speed limits and traffic laws could help reduce crashes. Intersections could use cameras to catch people who run red lights. Cities should make sure intersections have good traffic signals that are timed properly. Maybe in the future, self-driving cars that are super smart computers will help prevent accidents from happening at all.Another thing that creates traffic is roadwork and construction zones. It seems like there are always roads being repaired, buildings going up, or utilities like water/gas pipes being fixed underground. This forces the other traffic to merge into fewer lanes, which leads to huge backups. Construction is necessary, but cities need to coordinate these projects better to reduce the traffic impacts. They could require roadwork tohappen at night when there are fewer cars. Or maybe have strict schedules for different zones so that the whole city isn't under construction at once.Dealing with delivery trucks and freight vehicles is another part of solving traffic woes. More and more people are shopping online these days, which means there are sooooo many trucks out making deliveries. Those big trucks take up a lot of space on streets and contribute to traffic. Maybe there could be special "truck routes" that keep them off of certain roads during rush hours? Or having centralized areas where trucks drop everything off, and then smaller vans do the final local deliveries? Getting creative with new solutions for freight traffic is important.Lastly, we can use technology in smart ways to reduce traffic. Sensors could monitor traffic flows and adjust signal timings automatically to keep things moving. Overhead signs could directly trucks to take alternate routes if there is a jam ahead. Apps could analyze real-time traffic data and suggest best routes for individuals cars. Maybe one day our self-driving car computers will just automatically coordinate with each other to avoid traffic jams in the first place!Solving traffic congestion won't be easy, but we have to work on it for a better future. It's going to take cooperation fromeveryone - city governments have to make smart policies, transit agencies need to provide better services, businesses need to reduce truck traffic, and individuals need to drive less. As kids, we can do our part by walking, biking, taking buses, or car pooling as much as possible. We can also write letters to our city leaders pushing for more funding on traffic solutions. It's our world, so we need to be a part of fixing big problems like this traffic mess!。

0引言在电力现货市场中，能够准确预测电价对电力需求和供给以及优化调度有重要意义[1]。

电力市场参与者也需要准确地进行电价预测，达到投机、风险管控和战略决策实现利润最大化的目的。

2018年8月底，南方（以广东起步）电力现货市场启动试运行，以节点电价为基础确定广东现货市场的价格形成机制。

基于以上背景，本文研究实时电价历史数据以期提高预测精度，为电力市场交易决策提供有效依据。

在实时电价预测方面，由于实时电价具有非平稳性和非线性的特点，传统的预测方法难以达到令基于小波包分解和长短期记忆网络的短期电价预测刘达1，2，雷自强1，2，孙堃3（1.华北电力大学新能源电力与低碳发展研究中心，北京102206；2.华北电力大学智慧能源研究所，北京102206；3.华北电力大学经济与管理学院，北京102206）摘要：在电力市场环境下，精准的短期电价预测可以保障电网优化调度和安全稳定运行，但实时电价具有非平稳性和非线性的特点，加大了预测难度。

针对这一问题，提出了一种基于小波包分解（WPD）和长短期记忆（LSTM）网络的短期实时电价预测方法。

将实时电价序列分解，得到最高频细节部分和低频趋势部分，剔除波动性高、无效信息多的高频细节部分，再采用LSTM网络对有效信息最多、更能体现电价序列的趋势部分进行实时电价预测。

使用所提方法对美国PJM市场某地区实时电价数据进行预测实验，结果表明所提方法相比随机森林、BP神经网络、支持向量机电价预测方和传统的LSTM网络电价预测方法具有更高预测精度。

关键词：小波包分解；LSTM网络；短期电价预测；电力市场中图分类号：TM473文献标志码：A文章编号：2096-4145（2020）04-0077-07Short-term Electricity Price Forecasting Based on Wavelet Packet Decomposition&Long-Term and Short-Term Memory NetworksLIU Da1，2，LEI Ziqiang1，2，SUN Kun3（1.Key Laboratory of New Energy and Low-Carbon Development，North China Electric Power University，Beijing 102206，China2.Economics and Management School，North China Electric Power University，Beijing102206，China；3.Institute of Smart Energy，Beijing102206，China）Abstract:Accurate short-term electricity price forecasting can ensure optimal grid dispatching&safe and stable operation in the power market environment，but real-time electricity prices are characterized by non-stationary and non-linear characteristics，which increases the difficulty of forecasting.A short-term real-time electricity price forecasting method is proposed based on wavelet packet decomposition（WPD）and long-term short-term memory（LSTM）networks.The real-time electricity price sequence is decomposed to obtain the highest-frequency detail part and the low-frequency trend part，and the high-frequency detail part with high volatility and invalid information is removed.Then the LSTM network is used to forecast the real-time electricity price for the trend part which has the most effective information and can better reflect the electricity price sequence.The proposed method is used to predict the real-time electricity price data of a certain area in the US PJM market.The experimental results show that the proposed method has higher prediction accuracy compared with the random forest price forecasting model，BP neural network price forecasting method，support vector electromechanical price forecasting method and traditional LSTM network.Key words:wavelet packet decomposition；LSTM network；short-term electricity price forecast；electricity market基金项目：国家自然科学基金资助项目（71772060）Project Supported by the National Natural Science Foundation of China（71772060）人满意的效果。