Mode Choice Modeling Using Personalized Travel Time and Cost Data

Abstract Mode choice is a key module for long term travel demand studies (i.e. regional travel demand models), and also for many short term studies (e.g. congestion pricing, toll value analysis). In traditional four-step models, after trips are generated and distributed between zones, mode choice analysis is conducted, before the traffic assignment step, to find the share of each mode for passenger movement (McNally, 2008). In tour-based or activity-based models, the selection of mode for each trip (or tour) is conducted at different steps of the larger framework. For example, in many tour-based, and also activity-based models, similar to the four-step ones, the mode is determined once the tour is formed (McNally, 2007), however in some others, first the mode is selected and then the tour is formed. In more advanced travel demand models (i.e. dynamic activity-based models), the order of travel related choices for each individual (e.g. mode, destination, departure time, party composition) can vary from one to another. For example, in the ADAPTS (Agent-based Dynamic Activity Planning and Travel Scheduling) model, for each agent, a model is run to determine the order of choices and then each travel attribute is determined at the specified order (Auld and Mohammadian, 2012). Although mode choice is one of the most studied travel attributes (Train, 1976), if not the most, and there is a rich literature on developing models for this decision, the literature have mostly focused on examining different statistical methods and econometric techniques while the quality of the data to be used has not yet been sufficiently studied. A well designed survey could capture many details, events and conditions determinant in choosing the travel mode. However, in addition to the information captured through surveys, additional information from transportation related data sources (e.g. transportation network information, population and spatial information) should be collected and collated to complement survey data. There have been numerous attempts to improve the quality of surveys supporting mode choice models including revealed and stated preference data collection methods (Hensher, 1994). Nonetheless, the significance of the supplementary data sources coming from the transportation system is not often well studied. Especially, due to availability of information technology tools, data from the network can be spatially and temporally personalized for survey participants. This personalization significantly affects the mode choice model parameters because it provides more accurate information about the unobserved modes and complements the information obtained about the observed mode. This study uses the Chicago Metropolitan Agency for Planning (CMAP) Travel Tracker Survey data which is a comprehensive travel and activity survey, designed for use in a regional travel demand model and conducted over 14 months period began in January 2007. More than 14,000 households participated in the survey and recorded their activities and travels for one or two consecutive days. At the end, more than 218,005 activities were recorded. Public transportation in the Metropolitan of Chicago is provided by three major agencies including CTA (with both Bus and Subway fleet) which serves the city of Chicago residents, Pace system which mainly serves intra and inter suburban trips, as well as the suburb-city demand for travel, and finally Metra, which mainly gives service to suburbs to city commute trips with its heavy rail fleet of cars. To improve the accuracy of the transportation network data, this study focuses on the improvement of alternative modes generation for model estimation. To generate the alternative modes, instead of relying on TAZ level travel time skims, Google Maps and RTA’s Goroo TripPlanner website (http://tripsweb.rtachicago.com) are used to find each trip’s point to point travel times for alternative modes on the exact day of week and time of day that the original trip occurred as well as accurate transit access and egress distances and transfer information. Moreover, since all legs of the trips are known (when transit is used), the fare associated with each trip are accurately calculated (taking into account each agency’s complicated fare system). For driving mode, this process was repeated for a few weeks, and the average travel times are used. Additionally, several constraints regarding the availability of alternatives and resources have been carefully taken into account in this study to replicate the situation observed by the decision maker when choosing travel mode. To the best of the authors’ knowledge, conventional mode choice applications in the literature employ simplifying assumptions for resource availability. However, extensive data and database analysis are performed in this paper to target this important issue. As it was explained above, great effort was dedicated to more accurately simulate the conditions in which survey respondents made their mode choice decisions, including the collection of accurate trip information from Google Maps and a local travel planner website (for all modes), as well as extensive control on resource constraints. The survey database was complemented by this information. Using the updated database, two mode choice models are developed. The first model follows the conventional way of preparing mode choice data and the second one benefits from the advanced personalized database. In order to compare these two models both in terms of estimation and prediction potential, they are developed based on the same modeling structure as well as input data. The training sample is an 80% filtered random sample from the CMAP Travel Tracker Survey data, and tested on the remaining 20% data. Since the TAZ-level travel time skim information provided by CMAP does not specify the type of transit modes, the authors decided to use multinomial logit for the comparison step. In short, this study aims to present how misleading a mode choice model could be if the conventional simplifying methods are used to generate the choice set available to decision makers.Eventually, after comparing the two estimated models, a nested logit mode choice model is developed with the personalized data to represent a comprehensive mode choice model for the Chicago Metropolitan. References Auld J, Mohammadian A. (2012) Activity planning processes in the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model, Transportation Research Part A: Policy and Practice , 46(8):1386-403 Hensher, David A. (1994) Stated preference analysis of travel choices: the state of practice, Transportation , 21.2: 107-133 McNally MG (2008) The Four Step Model. UC Irvine: Center for Activity Systems Analysis McNally MG, Rindt CR (2007) The Activity-Based Approach. UC Irvine: Center 51 for Activity Systems Analysis Train, K. (1978) A validation test of a disaggregate mode choice model, Transportation Research , 12.3: 167-174.