DYNAMIC FREEWAY TRAVEL TIME PREDICTION USING SINGLE LOOP DETECTOR AND INCIDENT DATA

The accurate estimation of travel time is valuable for a variety of transportation applications such as freeway performance evaluation and real-time traveler information. Given the extensive availability of traffic data collected by intelligent transportation systems, a variety of travel time estimation methods have been developed. Despite limited success under light traffic conditions, traditional corridor travel time prediction methods have suffered various drawbacks. First, most of these methods are developed based on data generated by dual-loop detectors that contain average spot speeds. However, single-loop detectors (and other devices that emulate its operation) are the most commonly used devices in traffic monitoring systems. There has not been a reliable methodology for travel time prediction based on data generated by such devices due to the lack of speed measurements. Moreover, the majority of existing studies focus on travel time estimation. Secondly, the effect of traffic progression along the freeway has not been considered in the travel time prediction process. Moreover, the impact of incidents on travel time estimates has not been effectively accounted for in existing studies.The objective of this dissertation is to develop a methodology for dynamic travel time prediction based on continuous data generated by single-loop detectors (and similar devices) and incident reports generated by the traffic monitoring system. This method involves multiple-step-ahead prediction for flow rate and occupancy in real time. A seasonal autoregressive integrated moving average (SARIMA) model is developed with an embedded adaptive predictor. This predictor adjusts the prediction error based on traffic data that becomes available every five minutes at each station. The impact of incidents is evaluated based on estimates of incident duration and the queue incurred.Tests and comparative analyses show that this method is able to capture the real-time characteristics of the traffic and provide more accurate travel time estimates particularly when incidents occur. The sensitivities of the models to the variations of the flow and occupancy data are analyzed and future research has been identified.The potential of this methodology in dealing with less than perfect data sources has been demonstrated. This provides good opportunity for the wide application of the proposed method since single-loop type detectors are most extensively installed in various intelligent transportation system deployments

SPATIO-TEMPORAL DYNAMICS OF SHORT-TERM TRAFFIC

Short-term traffic forecasting and missing data imputation can benefit from the use of neighboring traffic information, in addition to temporal data alone. However, little attention has been given to quantifying the effect of upstream and downstream traffic on the traffic at current location. The knowledge about temporal and spatial propagation of traffic is still limited in the current literature. To fill this gap, this dissertation research focus on revealing the spatio-temporal correlations between neighboring traffic to develop reliable algorithms for short-term traffic forecasting and data imputation based on spatio-temporal dynamics of traffic. In the first part of this dissertation, spatio-temporal relationships of speed series from consecutive segments were studied for different traffic conditions. The analysis results show that traffic speeds of consecutive segments are highly correlated. While downstream traffic tends to replicate the upstream condition under light traffic conditions, it may also affect upstream condition during congestion and build up situations. These effects were statistically quantified and an algorithm for properly choosing the “best” or most correlated neighbor(s), for potential traffic prediction or imputation purposes was proposed. In the second part of the dissertation, a spatio-temporal kriging (ST-Kriging) model that determines the most desirable extent of spatial and temporal traffic data from neighboring locations was developed for short-term traffic forecasting. The new ST-Kriging model outperforms all benchmark models under various traffic conditions. In the final part of the dissertation, a spatio-temporal data imputation approach was proposed and its performance was evaluated under scenarios with different data missing rates. Compared against previous methods, better flexibility and stable imputation accuracy were reported for this new imputation technique

Development of dynamic recursive models for freeway travel time predictions

Traffic congestion has been a major problem in metropolitan areas, which is caused by either insufficient roadway capacity or unforesceable incidents. In order to promote the efficiency of the existing roadway networks and mitigate the impact of traffic congestion, the development of a sound prediction model for travel times is desirable. A comprehensive literature review about existing prediction models was conducted by investigating the advantages, disadvantages, and limitations of each model. Based on the features and properties of previous models, the base models including exponential smoothing model (ESM), moving average model (MAM), and Kalman filtering model (KFM) are developed to capture stochastic properties of traffic behavior for travel time prediction. By incorporating KFM into ESM and MAM, three dynamic recursive prediction models including dynamic exponential smoothing model (DESM), improved dynamic exponential smoothing model (JDESM), and dynamic moving average model (DMAM) are developed, in which the time-varying weight parameters are optimized based on the most recent observation. Model evaluation has been conducted to analyze prediction accuracy under various traffic conditions (e.g., free-flow condition, recurrent and non-recurrent congested traffic conditions). Results show that the IDESM in general outperforms other models developed in this study in prediction accuracy and stability. In addition, the feature and logic of the IDESM lead to its high transferability and adaptability, which could enable the prediction model to perform well at multiple locations and deal with complicated traffic conditions. Besides the proficient capability, the IDESM is easy to implement in the real world transportation network. Thus, the IDESM is proven an appealing approach for short-time travel time prediction under various traffic conditions. The application scope of the IDESM is identified, while the optimal prediction intervals are also suggested in this study

Bus arrival time prediction using stochastic time series and Markov chains

Public transit agencies rely on disseminating accurate and reliable information to transit users to achieve higher service quality and attract more users. With the development of new technologies, the concept of providing users with reliable information about bus arrival times at bus stops has become increasingly attractive. Due to the fact that bus operation parameters and variables are highly stochastic, modeling prediction of bus travel and arrival times has become one of the many challenging tasks. Stochastic time series and delay propagation models to predict bus arrival times using historical information were developed. Markov models were developed to predict propagation of bus delay to downstream bus stops based on heterogeneous conditions. The bus arrival times were predicted using a Markov model only and performance measures were obtained and a combined arrival time prediction model consisting of delay propagation and full autoregressive model was also developed. The inclusion of bus delay propagation into the bus arrival time prediction algorithm is an important contribution to the research efforts to predict bus arrival times. The research showed that Markov models can provide accurate bus arrival time predictions without increasing the need for a large number of bus operation variables, simulations and high polling frequency of the geographical bus location as used by other modeling approaches

Shape based classification and functional forecast of traffic flow profiles

This dissertation proposes a methodology for traffic flow pattern analysis, its validation, and forecasting. The shape of the daily traffic flows are directly related to the commuter’s traffic behavior which merit analysis based on their shape characteristics. As a departure from the traditional approaches, this research proposed a methodology based on shape for traffic flow analysis. Specifically, Granulometric Size Distributions (GSDs) were used to achieve classification of daily traffic flow patterns. A mathematical morphology method was used that allows the clustering of shapes. The proposed methodology leads to discovery of interesting daily traffic phenomena such as five normal daily traffic shapes beside abnormal shapes representing accidents, congestion behavior, peak time fluctuations, and malfunctioning sensors. To ascertain the significance of shape in traffic analysis, the proposed methodology was validated through a comparative classification analysis of the original data and GSD transformed data using the Back Prorogation Neural Network (BPNN). Results demonstrated that through shape based clustering more appropriate grouping can be accomplished that can result in better estimates of model parameters. Lastly, a functional time series approach was proposed to forecast traffic flow for short and medium-term horizons. It is based on functional principal components decomposition to forecast three different traffic scenarios. Real-time forecast scenarios of partially observed traffic profiles through Penalized Least squares (PLS) technique were also demonstrated. Functional methods outperform the conventional ARIMA model in both short and medium-term forecast horizons. In addition, performance of functional methods in forecasting beyond one hour was also found to be robust and consistent. --Abstract, page iii

Understanding Factors Affecting Arterial Reliability Performance Metrics

In recent years, the importance of travel time reliability has become equally important as average travel time. However, the majority focus of travel time research is average travel time or travel time reliability on freeways. In addition, the identification of specific factors (i.e., peak hours, nighttime hours, etc.) and their effects on average travel time and travel time variability are often unknown. The current study addresses these two issues through a travel time-based study on urban arterials. Using travel times collected via Bluetooth data, a series of analyses are conducted to understand factors affecting reliability metrics on urban arterials. Analyses include outlier detection, a detailed descriptive analysis of select corridors, median travel time analysis, assessment of travel time reliability metrics recommended by the Federal Highway Administration (FHWA), and a bivariate Tobit model. Results show that day of the week, time of day, and holidays have varying effects on average travel time, travel time reliability, and travel time variability. Results also show that evening peak hours have the greatest effects in regards to increasing travel time, nighttime hours have the greatest effects in regards to decreasing travel time, and directionality plays a vital role in all travel time-related metrics

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

Using Bluetooth to estimate traffic metrics for traffic management applications

PhD Thesis‘Bluetooth’ is a technology that can be integrated into Intelligent Transport Systems (ITS) to facilitate smarter and enhanced traffic monitoring and management to reduce congestion. The current research focus on Bluetooth is principally on journey time management. However, the applicability and viability of Bluetooth potential in problematic urban areas remains unknown. Besides the generic problem of unavailability of processing algorithms, there is gap in knowledge regarding the variability and errors in Bluetooth-derived metrics. These unknown errors usually cause uncertainty about the conclusions drawn from the data. Therefore, a novel Bluetooth-based vehicle detection and Traffic Flow Origin-destination Speed and Travel-time (TRAFOST) model was developed to estimate and analyse key traffic metrics. This research utilised Bluetooth data and other independently measured traffic data collected principally from three study sites in Greater Manchester, UK. The Bluetooth sensors at these locations generated vehicle detection rates (7-16%) that varied temporally and spatially, based on the comparison with flows from ATC (Automatic Traffic Counters) and SCOOT (Split Cycle Offset Optimisation Technique) detectors. Performance evaluation of the estimation showed temporal consistency and accuracy at a high level of confidence (i.e. 95%) based on criteria such as Mean Absolute Deviation (MAD) - (0.031 – 0.147), Root Mean Square Error (RMSE) - (0.041 – 0.195), Mean Absolute Percentage Error (MAPE) - (0.822 – 4.917) and Kullback-Leibler divergence (KL-D) (0.004 – 0.044). This outcome provides evidence of reliability in the results as well as justification for further investigation of Bluetooth applications in ITS. However, the resulting accuracy depends significantly on sample size, network characteristics, and traffic flow regimes. The Bluetooth approach has enabled a deeper understanding of traffic flow regimes and spatio-temporal variations within the Greater Manchester Networks than is possible using conventional traffic data such as from SCOOT. Therefore, the application of Bluetooth technology in ITS to enhance traffic management to reduce congestion is a viable proposition and is recommended.Petroleum Technology Development Fund (PTDF) – for the award of a PhD Scholarship for 4 years; The University of Lagos – for the study leave with pay; Surveyors Council of Nigeria (SURCON) – for the additional support

Performance Measures to Assess Resiliency and Efficiency of Transit Systems

Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jersey’s Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service. This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster