Trip distribution | EPFL Graph Search

Trip distribution (or destination choice or zonal interchange analysis) is the second component (after trip generation, but before mode choice and route assignment) in the traditional four-step transportation forecasting model. This step matches tripmakers’ origins and destinations to develop a “trip table”, a matrix that displays the number of trips going from each origin to each destination. Historically, this component has been the least developed component of the transportation planning model. Where: T ij = trips from origin i to destination j. Note that the practical value of trips on the diagonal, e.g. from zone 1 to zone 1, is zero since no intra-zonal trip occurs. Work trip distribution is the way that travel demand models understand how people take jobs. There are trip distribution models for other (non-work) activities such as the choice of location for grocery shopping, which follow the same structure. Over the years, modelers have used several different formulations of trip distribution. The first was the Fratar or Growth model (which did not differentiate trips by purpose). This structure extrapolated a base year trip table to the future based on growth, but took no account of changing spatial accessibility due to increased supply or changes in travel patterns and congestion. (Simple Growth factor model, Furness Model and Detroit model are models developed at the same time period) The next models developed were the gravity model and the intervening opportunities model. The most widely used formulation is still the gravity model. While studying traffic in Baltimore, Maryland, Alan Voorhees developed a mathematical formula to predict traffic patterns based on land use. This formula has been instrumental in the design of numerous transportation and public works projects around the world. He wrote "A General Theory of Traffic Movement," (Voorhees, 1956) which applied the gravity model to trip distribution, which translates trips generated in an area to a matrix that identifies the number of trips from each origin to each destination, which can then be loaded onto the network.

Exploiting pedestrian WiFi traces for destination choice modeling

Loïc Tinguely

My master thesis proposes a general methodology to model pedestrian destination choice from WiFi localization in multimodal transport facilities (e.g., airports, railway stations). It is based on the output of [Danalet, A., B. Farooq and M. Bierlaire (2014) A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures, Transportation Research Part C: Emerging Technologies, 44, 146–170.] method to generate candidates of activity-episode sequences from WiFi measurements, locations of activities on a map and prior information. Destination choice is nested to the activity choice. An individual first chooses an activity ([Danalet, A. and M. Bierlaire (2015) Importance sampling for activity path choice, paper presented at the 15th Swiss Transport Research Conference (STRC), Monte Verità, Ascona, Switzerland.]), and then selects the destination where to perform it. We propose an approach to model destination choice accounting for panel nature of data. We compare static, dynamic strict exogenous and dynamic model with two different agent effect corrections inspired by [Wooldridge, J. M. (2002) Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity, Journal of applied econometrics, 44, ISSN 1753-9196.] method. In a case study using WiFi traces on EPFL campus, we focus on one activity type: catering. The choice set contains 21 alternatives on campus (restaurants, self-services, cafeterias, ...). Our models reveal that the choice of a catering facility depends mostly on habits (e.g., where an individual ate the previous time), distance to walk from the previous activity-episode (calculated with a weighted shortest path algorithm) but less on destination specific determinants (e.g., price, capacity). The models are successfully validated using the same WiFi dataset and we forecast possible changes concerning catering destinations on the campus.

2015

Exploiting pedestrian WiFi traces for destination choice modeling

Loïc Tinguely

2015