RP and SP Data-Based Travel Time Reliabiality Analysis

Doctoral thesis English OPEN
Lu, Ming (2013)
  • Publisher: ETH
  • Related identifiers: doi: 10.3929/ethz-a-010140173
  • Subject: ROUTE PLANNING + ROUTE CHOICE (TRANSPORTATION AND TRAFFIC); VERKEHRSMODELLE + VERKEHRSSIMULATION (VERKEHR UND TRANSPORT); TRAVEL TIME + RUNNING TIME (TRANSPORTATION AND TRAFFIC); TRANSPORT MODELS + TRAFFIC SIMULATION (TRANSPORTATION AND TRAFFIC); VERKEHRSMITTELWAHL (WIRTSCHAFTSWISSENSCHAFTEN); NAVIGATIONSPLANUNG + ROUTENWAHL (VERKEHR UND TRANSPORT); CHOICE OF MEANS OF TRANSPORTATION (ECONOMICS); REISEZEIT + TRANSPORTDAUER (VERKEHR UND TRANSPORT) | Commerce, communications, transport
    • ddc: ddc:380

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 eects 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.</br>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.</br>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 indierence buers around the preferred arrival time. An exhaustive algorithm searches for the optimum early and late buer combinations and during the procedure, the changes of value of 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 buers of travel time to address travel time reliability eects on route choices.</br>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.
Share - Bookmark