A review of travel time estimation and forecasting for advanced traveler information systems

Providing on line travel time information to commuters has become an important issue for Advanced Traveler Information Systems and Route Guidance Systems in the past years, due to the increasing traffic volume and congestion in the road networks. Travel time is one of the most useful traffic variables because it is more intuitive than other traffic variables such as flow, occupancy or density, and is useful for travelers in decision making. The aim of this paper is to present a global view of the literature on the modeling of travel time, introducing crucial concepts and giving a thorough classification of the existing tech- niques. Most of the attention will focus on travel time estimation and travel time prediction, which are generally not presented together. The main goals of these models, the study areas and methodologies used to carry out these tasks will be further explored and categorized

A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

A Reliable Travel Time Prediction System With Sparsely Distributed Detectors

This study aims to develop a travel time prediction system that needs only a small number of reliable traffic detectors to perform accurate real-time travel time predictions under recurrent traffic conditions. To ensure its effectiveness, the proposed system consists of three principle modules: travel time estimation module, travel time prediction module, and the missing data estimation module. The travel time estimation module with its specially designed hybrid structure is responsible for estimating travel times for traffic scenarios with or without sufficient field observations, and for supplying the estimated results to support the prediction module. The travel time prediction module is developed to take full advantage of various available information, including historical travel times, geometric features, and daily/weekly traffic patterns. It can effectively deal with various traffic patterns with its multiple embedded models, including the primary module of a multi-topology Neural Network model with a rule-based clustering function and the supplemental module of an enhanced k-Nearest Neighbor model. To contend with the missing data issue, which occurs frequently in any real-world system, this study incorporates a missing data estimation module in the travel time prediction system, which is based on the multiple imputation technique to estimate both the short- and long-term missing traffic data so as to avoid interrupting the operations. The system developed in this study has been implemented with data from 10 roadside detectors on a 25-mile stretch of I-70 eastbound, and its performance has been tested against actual travel time data collected by an independent evaluation team. Results of extensive evaluation have indicated that the developed system is capable of generating reliable prediction of travel times under various types of traffic conditions and outperforms both state-of-the-practice and state-of-the-art models in the literature. Its embedded missing data estimation models also top existing methods and are able to maintain the prediction system under a reliable state when one of its detectors at a key location experience the data missing rate from 20% to 100% during uncongested, congested and transition periods

Contributions to time series data mining departing from the problem of road travel time modeling

194 p.Bidaiarientzako Informazio Sistema Aurreratuak (BISA) errepideetan sensoreenbidez bildutako datuak jaso, prozesatu eta jakitera ematen dituzte,erabiltzailei haien bidaietan lagunduz eta ibilbidea hasi baino lehen eta bideanhartu beharreko erabakiak erraztuz [5]. Helburu honetarako, BISA sistemektrafiko ereduak beharrezkoak dituzte, bidaiarientzat baliagarriak izandaitezkeen trafiko aldagaiak deskribatu, simulatu eta iragartzeko balio duelako.Zehazki, kontutan hartu daitezkeen trafiko aldagai guztietatik (fluxua,errepidearen okupazioa, abiadurak, etab.) bidai denbora da erabiltzaileentzatintuitiboena eta ulerterrazena den aldagaia eta, beraz, BISA sistemetan garrantziberezia hartzen duena [6]. Bidai denbora, aurrez zehaztutako puntubatetik bestera joateko ibilgailu batek behar duen denborari deritzo.Bidai denboren eredugintzan bi problema nagusi bereizten dira: estimazioaeta iragarpena. Nahiz eta literaturan batzuetan bi kontzeptu hauek baliokidetzatjo, berez, bi problema bereizi dira, ezaugarri eta helburu ezberdinekin,eta teknika ezberdinak eskatzen dituztenak.Alde batetik, bidai denboren estimazioaren helburua iada amaitutakobidaietan ibilgailuak bataz beste zenbat denbora igaro duten kalkulatzeada. Horretarako, ibilbidean zehar jasotako trafikoari buruzko informazioaedo/eta bestelako datuak (eguraldia, egutegiko informazioa, etab.) erabildaitezke [1]. Estimazio metodo ezberdinak eskuragarri dauden datu motaeta kantitatearen araberara sailka daitezke eta, a posteriori motako balorazioakegiteko balio dute. Bestalde, bidai denboren iragarpena, orainean edoetorkizunean hasiko diren bidaien denborak kalkulatzean datza. Honetarako,iragarpena egiten den momentuan jasotako eta iraganeko trafikoari buruzkodatuak eta testuinguruko informazioa erabiltzen da [8].Ibilgailu kopuru eta auto-ilaren ugaritzeen ondorioz, bidai denboren estimazioeta predikzio onak lortzea geroz eta beharrezkoagoa da, trafikoarenkudeaketa egokia ahalbidetzen duelako. Hau ikusirik, azken urteetan eredumota ezberdin andana proposatu eta argitaratu dira. Nolanahi ere, literaturarenberrikuspen eta analisi sakon bat egin dugu tesi honen lehenengoatalean. Bertan, ondorioztatu ahal izan dugu proposatutako eredu guztiakez direla egokiak errepide sare, trafiko egoera eta datu mota guztiekin erabiltzeko.Izan ere, atera dugun ondorio nabariena, argitaratutako eredu askokez dituztela BISA sistemen eskakizun praktikoak betetzen, da. Lehenik etabehin, eredu asko errepide zati txikietan soilik aplika daitezke, eta ez dagoargi errepide sare guztira nola hedatu daitezkeen. Bestalde, eredu gehienekdatu mota bakarra erabiltzen dute eta errealitatean ohikoa da datu mota batekinbaina gehiagorekin lan egin behar izatea. Azkenik, pilaketa ez-ohikoenaurrean malgutasun mugatua izatea ere desabantaila nabari eta ohikoa da.Hau honela, eredu konbinatu edo hibridoak proposamen hauetatik guztietatiketorkizun handiena dutenak direla dirudi, patroi ezberdinetara moldatzekogaitasuna dutelako, eta eredu eta datu mota ezberdinak nahastekoaukera ematen dutelako.Tesi honetan, bidai denborak iragartzeko eredu hibrido edo konbinatuakhartuko ditugu abiapuntutzat. Zehazki, hasieran datuak antzekotasunarenarabera multzokatzen dituenetan jarriko dugu arreta. Metodo hauek, datuakmultzokatu ondoren, multzo bakoitzari bidai denborak iragartzeko eredu ezberdinbat aplikatzen diote, zehatzagoa eta patroi espezifiko horrentzat espresukieraikia.Eredu talde honen kasu berezi bat, datuen multzokatzea denbora serieentaldekatzearen bitartez egiten duena da. Denbora serieen taldekatzea (clustering-a ingelesez) datu mehatzaritzako gainbegiratu gabeko ataza bat da, nonhelburua, denbora serie multzo, edo beste era batera esanda, denbora seriedatu base bat emanik, serie hauek talde homogeneoetan banatzea den [3]. Xedea,beraz, talde bereko serieen antzekotasuna ahalik eta handiena izatea etaaldiz, talde ezberdinetako serieak ahalik eta desberdinenak izatea da. Trafikodatuetan eta bidai denboretan, portaera ezberdinetako egunak aurkitzea osoohikoa da (adib. asteguna eta asteburuak). Hau honela, egun osoan zeharjasotako bidai denborez osatutako serie bat izanik, metodo mota honek lehenik,dagokion egun mota identifikatuko luke eta ondoren iragarpenak egunmota horretarako bereziki eraikitako eredu batekin lortuko lituzke.Denbora serieen clustering-an oinarritutako eredu mota hau ez da ia inoizerabili literaturan eta, ondorioz, bere onurak eta desabantailak ez dira ondoaztertu orain arte. Honegatik, tesi honen bigarren kapituluan, eredugintzaprozeduaren hasieran egun mota ezberdinak identifikatzea bidai denboreniragarpenak lortzeko lagungarria ote den aztertu dugu, emaitza positiboaklortuz. Hala ere, praktikan, honelako eredu konbinatuak eraikitzeak eta erabiltzeakzailtasun bat baino gehiago dakartza. Tesi honetan bi arazo nagusietanjarriko dugu arreta eta hauentzat soluzio bana proposatzea izango duguhelburu.Hasteko, denbora serieak multzokatzeko, erabaki ez tribial batzuk hartubehar dira, adibidez distantzia funtzio egoki bat aukeratzea. Literaturanbehin baino gehiagotan erakutsi da erabaki hau oso garrantzitsua dela etaasko baldintzatzen dituela lortuko diren emaitzak [7]. Trafikoko kasuan ere,hau honela dela demostratu dugu. Baina distantzia baten aukeraketa ez dabatere erraza. Azken urteotan hamaika distantzia ezberdin proposatu dituikerlari komunitateak denbora serieekin lan egiteko eta, dirudienez, datu basebakoitzaren ezaugarrien arabera, bat ala bestea izaten dela egokiena [3, 7].Guk dakigula, ez dago metodologia formalik erabiltzaileei aukeraketa hauegiten laguntzen dionik, ez batik bat denbora serieen clustering-aren testuinguruan.Metodologia ohikoena distantzia sorta bat probatzea eta lortutakoemaitzen arabera bat aukeratzea da. Zoritxarrez, distantzia batzuen kalkuluakonputazionalki oso garestia da, eta beraz, estrategia hau ez da batereeraginkorra praktikan.Ataza hau simplifikatzeko asmoarekin, tesiko hirugarren kapituluan etiketaanitzeko sailkatzaile bat (ingelesez multi-label classifier ) proposatzen dugudenbora serieen datu base bat multzokatzeko, distantzia egokiena modu automatikoanaukeratzen duena. Sailkatzaile hau eraikitzeko, hasteko, denboraserie datu base baten alderdi batzuk deskribatzeko ezaugarri sorta bat definitudugu. Besteak beste, datuetan dagoen zarata maila, autokorrelazio maila,serie atipikoen kopurua, periodizitatea eta beste hainbat ezaugarri neurtu etakuantifikatzeko metodoak proposatu ditugu. Ezaugarri hauek sailkatzaileakbehar duen input informazioa edo, bestela esanda, sailkatzailearen menpekoaldagaiak izango dira. Emaitza gisa, sailkatzaileak datu base batentzategokienak diren distantziak itzuliko dizkigu, kandidatu sorta batetik, noski.Sailkatzaile honen baliagarritasuna egiaztatzeko, esperimentu sorta zabalbat bideratu dugu, bai lan honetarako bereziki sortutako datu base sintetikoekineta bai UCR artxiboko [4] benetako datuak erabiliz. Lortutako emaitzapositiboak argi uzten dute proposatutako sailkatzaileak denbora serie batmultzokatzeko distantzia funtzio baten aukeraketa errazteko balio duela.Ekarpen hau azalduta, berriz bidai denboren iragarpenerako eredu kon-binatuetara itzuli eta bigarren problema bat identifikatzen dugu, tesiko bigarrenekarpen nagusira eramango gaituena. Gogoratu eredu konbinatu hauekhasiera batean datuak multzokatzen dituztela, clustering algoritmoak erabiliz.Talde bakoitzak patroi edo trafiko portaera ezberdin bat adieraziko du.Ondoren, talde bakoitzean iragarpenak egiteko, iragarpen eredu ezberdin bateraikiko dugu, soilik multzo horretako datu historikoak erabiliz. Gure kasuan,denbora serieen clustering-a aplikatu dugu eta beraz, egun mota ezberdinaklortuko ditugu. Ondoren, iragarpen berriak egin nahi ezkero, egun berri bathasten denean, zein multzokoa den asmatu beharko dugu, erabili behar duguneredua aukeratzeko.Ohartu, iragarpenak egiteko garaian, ez dugula egun osoko daturik izangoeskuragarri. Adibidez, goizeko hamarretan, eguerdiko hamabietan (2 ordugeroago) puntu batetik bestera joateko beharko dugun denbora iragarri nahibadugu, soilik egun horretan hamarrak arte jasotako informazioa izango dugueskuragarri, informazio historikoarekin batera, noski. Egoera honetan, egunhorretako informazio partzialarekin, seriearen lehen zatiarekin soilik, erabakibehar dugu zein multzotakoa den. Noski, ordurarte jasotako informazioa ezbada nahikoa adierazgarria, kalterako izan daiteke multzo eta eredu zehatzbat aukeratzea, eta ziurrenik hobe izango da eredu orokorrago bat erabiltzea,datu historiko guztiekin eraikia. Finean, egun berriak ahal bezain prontomultzo batera esleitu nahi ditugu, baina esleipen hauetan ahal bezain erroregutxien egin nahi dugu.Logikoa da pentsatzea esleipenak geroz eta lehenago eginez akatsak egitekoaukera handiagoa dela. Hau honela, helburua esleipenak ahal bezain azkaregitea da, baina zehaztasun maila onargarri bat bermatuz. Denbora serieenmehatzaritzan problema honi denbora serieen sailkapen goiztiarra (ingelesezearly classification of time series) deritzo [10].Denbora serieen sailkapena (ingelesez time series classification) [9, 10] datumehatzaritzako gainbegiratutako problema aski ezaguna da non, denboraserie multzo bat eta haietako bakoitzaren klasea jakinik, helburua sailkatzailebat eraikitzea den, serie berrien klaseak iragartzeko gai dena.Denbora serieen sailkapenaren azpi-problema gisa, sailkapen goiztiarra,denboran zehar iristen den datu zerrenda bat ahalik eta lasterren klase zehatzbatean sailkatzeko nahia edo beharra dagoenean agertzen da [10]. Adibide gisa,informatika medikoan, gaixoaren datu klinikoak denboran zehar monitorizatueta jasotzen dira, eta gaixotasun batzuen detekzio goiztiarra erabakigarriada pazientearen egoeran. Esaterako, arterien buxadura, fotopletismografia(PPG) serieen bidez detektatzen da errazen [2], baina diagnosian segunduhamarren baten atzerapenak, guztiz ondorio ezberdinak ekar ditzake.Honela, tesiaren 4. kapituluan, denbora serieen datu mehatzaritzari bigarrenekarpen garrantzitsu bezala, ECDIRE (Early Classification frameworkfor time series based on class DIscriminativeness and REliability ofpredictions) izeneko denbora serieen sailkatzaile goiztiarra aurkeztu dugu.Sailkatzaile hau eraikitzeko, entrenamendu fasean, metodoak klase bakoitzaanalizatzen du eta beste klaseengandik noiztik aurrera ezberdindu daitekeenkalkulatzen du, aurrez ezarritako zehaztasun maila bat mantenduz,noski. Zehaztasun maila hau erabiltzaileak finkatuko du haren interesen arabera.Entrenamentu fase honetan lortutako informazioak sailkapenak noizegin zehaztuko digu eta, beraz, serieak goizegi esleitzea saihesten lagundukodu. Bestalde, ECDIRE metodoak sailkatzaile probabilistikoak erabiltzen ditu,eta sailkatzaile mota hauengandik lortutako a-posteriori probabilitateak,lortutako sailkapenen zehaztasuna beste era batean kontrolatzen lagundukodigu.ECDIRE metodoa UCR artxiboko 45 datu baseei aplikatu diogu, literaturanorain arte lortutako emaitzak hobetuz. Bestalde, kasu erreal bateanmetodoaren aplikazioa nolakoa izango zen erakusteko, kantuen bidezko txoriendetekzio eta identifikazio problema baterako sortutako datu base batekinere burutu ditugu esperimentuak, emaitza egokiak lortuz.Azkenik, berriro ere bidai denboren iragarpenera itzuli gara eta aurrekobi ekarpenak problema honi aplikatu dizkiogu. Lortutako emaitzetatik,problema zehatz honetarako, proposatutako bi metodoetan egin beharrekomoldaketa batzuk identifikatu ditugu. Hasteko, distantzia aukeratzeaz gain,hauen parametroak ere aukeratu behar dira. Hau egiteko silhouette bezalakoindizeak erabili ditugu, baina argitzeke dago ea metodo hau ataza honetarakoonena den. Bestalde, datuen garbiketa eta aurre-prozesatze sakon bat beharrezkoadela ere ikusi dugu, serie atipikoak eta zaratak clustering soluzioetaneragin handia baitaukate. Azkenik, gure esperimentuak iragarpen eredu historikosimpleetan oinarritu ditugu. Eredu simple hauek ordu berdinean jasotakobidai denboren batez bestekoa kalkulatuz egiten dituzte iragarpenak,eta eredu konplexuagoak erabiltzea aukera interesgarria izan daiteke.Laburbilduz, tesi honetan bidai denboren eredugintzaren literaturarenanalisi batetik hasi gara eta, bertatik abiatuta, denbora serieen mehatzaritzaribi ekarpen egin dizkiogu: lehena, denbora serie multzo bat taldekatzekodistantzia automatikoki aukeratzeko metodo baten diseinua, eta bigarrena,sailkatzaile probabilistikoetan oinarritutako denbora serieen sailkatzaile goiztiarbat. Azkenik, berriro ere bidai denboren eredugintzaren problemara itzuligara eta aurreko bi ekarpenak testuinguru honetan aplikatuko ditugu, etorkizunerakoikerketa ildo berriak zabalduz

Traffic Surveillance and Automated Data Extraction from Aerial Video Using Computer Vision, Artificial Intelligence, and Probabilistic Approaches

In transportation engineering, sufficient, reliable, and diverse traffic data is necessary for effective planning, operations, research, and professional practice. Using aerial imagery to achieve traffic surveillance and collect traffic data is one of the feasible ways that is facilitated by the advances of technologies in many related areas. A great deal of aerial imagery datasets are currently available and more datasets are collected every day for various applications. It will be beneficial to make full and efficient use of the attribute rich imagery as a resource for valid and useful traffic data for many applications in transportation research and practice. In this dissertation, a traffic surveillance system that can collect valid and useful traffic data using quality-limited aerial imagery datasets with diverse characteristics is developed. Two novel approaches, which can achieve robust and accurate performance, are proposed and implemented for this system. The first one is a computer vision-based approach, which uses convolutional neural network (CNN) to detect vehicles in aerial imagery and uses features to track those detections. This approach is capable of detecting and tracking vehicles in the aerial imagery datasets with a very limited quality. Experimental results indicate the performance of this approach is very promising and it can achieve accurate measurements for macroscopic traffic data and is also potential for reliable microscopic traffic data. The second approach is a multiple hypothesis tracking (MHT) approach with innovative kinematics and appearance models (KAM). The implemented MHT module is designed to cooperate with the CNN module in order to extend and improve the vehicle tracking system. Experiments are designed based on a meticulously established synthetic vehicle detection datasets, originally induced scale-agonistic property of MHT, and comprehensively identified metrics for performance evaluation. The experimental results not only indicate that the performance of this approach can be very promising, but also provide solutions for some long-standing problems and reveal the impacts of frame rate, detection noise, and traffic configurations as well as the effects of vehicle appearance information on the performance. The experimental results of both approaches prove the feasibility of traffic surveillance and data collection by detecting and tracking vehicles in aerial video, and indicate the direction of further research as well as solutions to achieve satisfactory performance with existing aerial imagery datasets that have very limited quality and frame rates. This traffic surveillance system has the potential to be transformational in how large area traffic data is collected in the future. Such a system will be capable of achieving wide area traffic surveillance and extracting valid and useful traffic data from wide area aerial video captured with a single platfor

Adapting Traffic Simulation for Traffic Management: A Neural Network Approach

Static models and simulations are commonly used in urban traffic management but none feature a dynamic element for near real-time traffic control. This work presents an artificial neural network forecaster methodology applied to traffic flow condition prediction. The spatially distributed architecture uses life-long learning with a novel adaptive Artificial Neural Network based filter to detect and remove outliers from training data. The system has been designed to support traffic engineers in their decision making to react to traffic conditions before they get out of control. We performed experiments using feed-forward backpropagation, cascade-forward back-propagation, radial basis, and generalized regression Artificial Neural Networks for this purpose. Test results on actual data collected from the city of Leicester, UK, confirm our approach to deliver suitable forecasts

Advanced Quantitative Methods for Imminent Detection of Crash Prone Conditions and Safety Evaluation

Crashes can be accurately predicted through reliable data sources and rigorous statistical models; and prevented through data-driven, evidence-based traffic control strategies. Both predictive analysis and analysis to estimate the causal effect of traffic variables of real-time crashes are instrumental to crash prediction and a better understanding of the mechanism of crash occurrence. However, the research on the second analysis type is very limited for real-time crash prediction; and the conventional predictive analysis using inductive loop detector data has accuracy issues related to inconsistently and distantly spaced loop detectors. The effectiveness of traffic control strategies for improving safety performance cannot be measured and compared without an appropriate traffic simulation application. This dissertation is an attempt to address these research gaps. First, it conducts the propensity score based analysis to assess the causal effect of speed variation on crash occurrence using the crash data and ILD data. As a casual analysis method, the propensity score based model is applied to generate samples with similar covariate distributions in both high- and low-speed variation groups of all cases. Under this setting, the confounding effects are removed and the causal effect of speed variation can be obtained. Second, it conducts a predictive analysis on lane-change related crashes using lane-specific traffic data collected from three ILD stations near a crash location. The real-time traffic data for the two lanes – the vehicle’s lane (subject lane) and the lane to which that a vehicle intends to change (target lane) – are more closely related with lane-change related crashes, as opposed to congregated traffic data for all lanes. It is found that lane-specific variables are appropriate to study the lane-change frequency and the resulting lane-change related crashes. Third, it conducts a predictive analysis on real-time crashes using simulated traffic data. The purpose of using simulated traffic data rather than real data is to mitigate the temporal and spatial issues of detector data. The cell transmission model (CTM), a macroscopic simulation model, is employed to instrument the corridor with a uniform and close layout of virtual detector stations that measure traffic data when physical stations are not available. Traffic flow characteristics at the crash site are simulated by CTM 0-5 minutes prior to a crash. It shows that the simulated traffic data can improve the prediction performance by accounting for the spatial-tempo issue of ILD data. Fourth, it presents a novel approach to modeling freeway crashes using lane-specific simulated traffic data. The new model can not only account for the spatial-tempo issues of detector data but also account for heterogeneous traffic conditions across lanes using a lane-specific cell transmission model (LSCTM). The LSCTM illustrates both discretionary lane-changing (DLC) and mandatory lane-changing (MLC) activities. This new approach presents a viable alternative for utilizing traffic simulation models for safety analysis and evaluation. Last, it develops a crash prediction and prevention application (CPPA) based on simulated traffic data to detect crash-prone conditions and to help select the desirable traffic control strategies for crash prevention. The proposed application is tested in a case study with VSL strategies, and results show that the proposed crash prediction and prevention method could effectively detect crash-prone conditions and evaluate the safety and mobility impacts of various VSL alternatives before their deployment. In the future, the application will be more user-friendly and can provide both online traffic operations support as well as offline evaluation of various traffic control operations and methods