RP AND SP DATA-BASED TRAVEL TIME RELIABILITY ANALYSIS
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1 DISS. ETH NO RP AND SP DATA-BASED TRAVEL TIME RELIABILITY ANALYSIS A thesis submitted to attain the degree of DOCTOR OF SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich) presented by MING LU Master of Civil Engineering, Tongji University, Shanghai born on citizen of China accepted on the recommendation of Prof. Dr. Kay W. Axhausen, examiner Prof. Dr. Michael Bell, co-examiner Dr. Klaus Noekel, co-examiner 2013
2 Abstract Travel time is considered to be the key criterion when making travel related decisions. As the travel decisions are made in a dynamic environment, the travel time also changes according to the real-time operations of the transport system. More and more evidence proves that travellers are not only interested in the expected travel time but also in travel time reliability. Especially for trips that are made regularly, reliability is valued more than travel time itself. This dissertation focuses on travel time reliability measures and their effects on travel related decisions as well as network performance. Travel time is studied both at the aggregate level and the disaggregate level. At the aggregate level, personal preferences of travellers towards travel time are explored; while on the disaggregate level, the network performance is evaluated based on travel time reliability. Instead of defining a new travel time reliability index, the travel time distribution is used to address the variation of the travel time and its influences on both travellers at the micro level and the network at the macro level. Both revealed preference (RP) data and stated preference (SP) data are used for the analysis of the travel time reliability. The SP data provided two scenarios based on mode choice and route choice respectively and the data was collected in Switzerland. Proceeding on the SP data, parts of the RP data is also collected in Switzerland, which is later used to reconstruct the actual route choices of the respondents for a route choice model. Tomtom Stats data is obtained to assist the travel time reliability analysis during the procedure. Another part of the RP data, the floating car data (FCD), was collected from Wuhan, China and it is applied to employ network travel time reliability. Route choice models are built with travel time measures using both SP data and RP data. In the SP data based route choice model, as the travel time distribution was applied to generate the route alternatives, it allows us to explore the early and late indifference buffers around the preferred arrival time. An exhaustive algorithm searches for the optimum early and late buffer combinations and during the procedure, the changes of value of
3 Abstract travel time savings, value of reliability early and late along with the model fit are closely observed. The RP data based route choice model tries to explain the respondents route choices reconstructed from the RP data with predicted travel time reliability measures. First regression models are employed to predict parameters of the travel time distribution based on Tomtom Stats data and then these models are applied to predict travel time reliabilities for each route alternative. A Path Size Logit model is used to account for similarities between route alternatives with percentage of early and late buffers of travel time to address travel time reliability effects on route choices. FCD data is used to analyse travel time reliability on the network. Three levels of travel time reliability: link level, path level and network level are explored. On the link level, the travel time reliability is closely related to the speed changes along the road so link (un)reliability is defined as the integral of speed changes along the link. On the path level, Monte Carlo simulation is used to obtain the path travel time and the travel time distribution is extracted. The definition of a degradable network is given and a spatial auto regression model is built to calculate expected total network travel time and its variance. Then using method of moments estimation, the total network travel time distribution is reconstructed, from a degradable network as well as an undegradable network. x
4 Zusammenfassung Reisezeit gilt als entscheidendes Kriterium, wenn man die damit verbundenen Reiseentscheidungen trifft. Wahrend die Reiseentscheidungen in einem dynamischen Umfeld gemacht werden, so ändert sich auch die Reisezeit gemäß dem Real-time Betrieb des Transportsystems. Immer mehr Beweise belegen, dass Reisende, sich nicht nur für die erwartete Reisezeit interessieren, sondern auch für die Zuverlässigkeit der Reisezeit. Im speziellen für regelmäßige gemachte Reisen ist die Zuverlässigkeit wichtiger als die tatsächliche Reisezeit. Diese Dissertation konzentriert sich auf die Zuverlässigkeit der Reisezeit Maßnahmen und ihre Auswirkungen auf die Reiseentscheidungen sowie Netzwerk Leistung. Reisezeit ist sowohl auf der aggregierten Ebene und der kleinräumigen Ebene untersucht. Auf der aggregierten Ebene, persönliche Präferenzen Reisender werden zu der Reise- Zeit erforscht, während auf der kleinräumigen Ebene die Netzwerkleistung auf Basis Reisezeit und Zuverlässigkeit beurteilt wird. Statt einen neuen Zuverlässigkeits Index zu definieren, wird die Verteilung der Reisezeit benutzt um die Reisevariation und ihre Beeinflussung, auf beide, die Reisenden im Micro Level und das Netzwerk im Macro Level. RP Daten und SP Daten für die Analyse der Reisezeit Zuverlässigkeit sind verwendet. Die SP-Daten haben zwei Szenarien, eine für die Verkehrsmittelwahl und eine für Routenwahl. Die Daten wurden in der Schweiz gesammelt. Die Routen der Antwortenden wurde auch gesammelt und später in einem Modell verwendet. Tomtom Stats Daten wurde erhalten, um die Reisezeit Zuverlässigkeitsanalyse während des Verfahrens zu unterstützen. Der anderen Teil der RP-Daten, die Floating Car Data (FCD), wurde von Wuhan, China gesammelt und angewandt, um die Netzwerkzuverlässigkeit zu studieren. Es gibt zwei Modelle der Routenwahl,ein mit SP Daten und der andere mit RP Daten. In der SP-Daten basierend Modell, glauben wir, dass eine kurze Abstand bevor und nach der gewünschten Ankunftszeit keine Strafe erbringt. Da muss man finden, wie große diese zwei Abständen sind. Eine erschöpfende Algorithmus sucht nach der optimalen frühen und späten Pufferkombinationen für das Modell. Inzwischen werden die Änderungen
5 Zusammenfassung der VTTS, VoR-early und VoR-late beobachtet. Das RP-Daten basierend Modell analysiert die Daten mit ähnliche Methoden. Weil jede Route verschiedenen Fahrzeiten hat, wird die Anteile der Durchschnitts der Reisezeit benutzt. Eine Path Size Modell wird beurteilt, um die Ähnlichkeit der Routen zu lösen. FCD Daten von Wuhan sind verwendet, um Reisezeit Zuverlässigkeit im Netzwerk zu analysieren. Drei Ebenen der Reisezeit, Link-Ebene, Path- Ebene und Netzwerk-Ebene sind definiert. Auf der Link-Ebene gilt die Verlässlichkeit als Integral der Geschwindigkeitsänderungen entlang der Strecke. Auf dem Path-Ebene, Monte Carlo Simulation wird verwendet, um den Path Reisezeit zu erhalten, und die Verteilung der Reisezeit wird auch extrahiert. Auf der Netzwerk-Ebene, ein Spatial Regression Modell ist gebaut, um Durchschnitt und Variante der Reisezeit zu voraussagen. Die Methode der Moment Einschätzung ist verwendet, da kann man Verteilung der Reisezeit rekonstruieren. Und diese Methode gilt zwei verschiedenen Netzwerk, eine mit die Änderung der Verkehrsablauf und eine ohne. Die Resultaten von beide Netzwerk werden auch vergleicht. xii
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