Matka-aikaennustemenetelmä kysyntäohjatussa joukkoliikennepalvelussa

Short term travel time prediction system is an essential component of a modern large scale demand responsive transport system. Travel time prediction methods as well as different traffic data sources have been under an active research during the last decade. The study of travel time prediction methods has mainly focused on predicting a link travel time on expressways or inner city arterials based on fixed location loop detector data or automatic vehicle identification data. Also, usefulness of CPS based floating car data has been investigated. The routing system of demand responsive transport service requires travel time information that covers most of the roads in a city road network instead of the most important roads. This thesis compares travel time estimates that are extracted from sparsely sampled CPS based floating car data to the ground truth travel time data from licence plate cameras. A method that extracts link travel times from floating car data is developed. Method is applied by selecting the link travel time table by the time of the day. An essential finding is that it is possible to extract valuable city-wide travel time tables from a large CPS data set with a variable level of quality.Lyhyen aikavälin matka-aikaennustejärjestelmä on keskeinen osa laajan mittakaavan kysyntäohjatun liikenteen ohjausjärjestelmää. Matka-aikaennustemenetelmiä ja liikennedatalähteitä on tutkittu paljon viimeisen vuosikymmenen aikana. Menetelmätutkimus on pääosin hyödyntänyt moottoriteille ja sisääntuloväylille asennetuista kiinteistä induktiosilmukoista ja rekisterikilpikameroista saatavaa liikennedataa. GPS-pohjaisen ns. kelluvan auton datan hyödyntäminen on kasvanut merkittävästi. Kysyntäohjautuvan liikenteen reititysjärjestelmä tarvitsee matka-aikatietoa koko palvelualueelta tärkeimpien kaupungin liikennetilannetta kuvaavan väylän sijasta. Tämä diplomityö vertailee harvaan näytteistetystä GPS pohjaisesta kelluvan auton datasta purettua matka-aikatietoa olemassa olevaan rekisterikilpikameratietoon. Työssä kehitetään menetelmä GPS-tiedon muuntamiseksi tieverkkograafin kaarikohtaiseksi matka-aikatiedoksi. Menetelmää sovelletaan käyttämällä kellonaikaa ennustavana muuttujana. Työn keskeinen havainto on, että laajasta mutta laadultaan vaihtelevasta GPS-tiedosta on mahdollista päätellä liikennejärjestelmän ruuhkatilaa kuvaava matka-aikataulukko koko kaupungin laajuudelle

An integrated method for short-term prediction of road traffic conditions for intelligent transportation systems applications

The paper deals with the short-term prediction of road traffic conditions within Intelligent Transportation Systems applications. First, the problem of traffic modeling and the potential of different traffic monitoring technologies are discussed. Then, an integrated method for short-term traffic prediction is presented, which integrates an Artificial Neural Network predictor that forecasts future states in standard conditions, an anomaly detection module that exploits floating car data to individuate possible occurrences of anomalous traffic conditions, and a macroscopic traffic model that predicts speeds and queue progressions in case of anomalies. Results of offline applications on a primary Italian motorway are presented

[[alternative]]A Study of Travel Time Prediction Models in Transportation Networks (I)

計畫編號：NSC91-2211-E032-019研究期間：200208~200307研究經費：362,000[[sponsorship]]行政院國家科學委員

An artificial neural network model for predicting freeway work zone delays with big data

Lane closures due to road reconstruction and maintenance have resulted in a major source of non-recurring congestion on freeways. It is extremely important to accurately quantify the associated mobility impact so that a cost-effective work zone schedule and an efficient traffic management plan can be developed. Therefore, the development of a sound model for predicting delays or road users is desirable. A comprehensive literature review on existing work zone delay prediction models (i.e., deterministic queuing model and shock wave model) is conducted in this study, which explores the advantages, disadvantages, and limitations of different modeling approaches. The performance of those models seems restricted to predict congestion impact under space-varying (i.e., road geometry, number of lanes, lane width, etc.) and time-varying (i.e., traffic volume) conditions. To advance the delay prediction accuracy, a multivariate non-linear regression (MNR) model is developed first by incorporating big data to capture the relationship of speed versus the ratio of approaching traffic volume to work zone capacity for work zone delay prediction. The MNR model demonstrates itself able to predict spatio-temporal delays with reasonable accuracy. A more advanced model called ANN-SVM is developed later to further improve the prediction accuracy, which integrates a support vector machine (SVM) model and an artificial neural network (ANN) model. The SVM model is responsible to predict work zone capacity, and the ANN model is responsible to predict delays. The ultimate goal of ANN-SVM aims to predict spatio-temporal delays caused by a work zone on freeways in the statewide of New Jersey subject to road geometry, number of lane closure, and work zone duration in different times of a day and days of a week. There are 274 work zones with complete information for the proposed model development, which are identified by mapping data from different sources, including OpenReach, Plan4Safety, New Jersey Straight Line Diagram (NJSLD), New Jersey Congestion Management System (NJCMS), and INRIX. Big data analytics is used to examining this massive data for developing the proposed model in a reliable and efficient way. A comparative analysis is conducted by comparing the ANN-SVM results with those produced by MNR, RUCM (NJDOT Road User Cost Manual approach), and ANN-HCM (the ANN model with work zone capacity suggested by Highway Capacity Manual). It is found that ANN-SVM in general outperforms other models in terms of prediction accuracy and reliability. To demonstrate the applicability of the proposed model, an analysis tool, which adapts to ANN-SVM, is developed to produce graphical information. It is worth noting that the analysis tool is very user friendly and can be easily applied to assess the impact of any work zones on New Jersey freeways. This tool can assist transportation agencies visualize bottlenecks and congestion hot spots caused by a work zone, effectively quantify and assess the associated impact, and make suitable decisions (i.e., determining the best starting time of a work zone to minimize delays to the road users). Furthermore, ANN-SVM can be applied to develop, evaluate, and improve traffic management and congestion mitigation plans and to calculate contractor penalty based on cost overruns as well as incentive reward schedule in case of early work competition