Andreas Frei

Impact of crime statistics on travel mode choice: Case Study of the city of Chicago, Illinois

Whether crime or the perception of it has any direct and significant influence on travelers’ mode choice is a topic for which the evidence remains inconclusive. Studies have revealed various, and in some cases counterintuitive, roles that safety concerns can play in individuals’ travel behavior. In addition, characteristics of the physical environment such as land use and walkability are also influential factors in travelers’ decisions. This study explored these questions through the study of individual travel behavior by using discrete choice models applied to the reported home-based work trips in the Chicago household travel survey. Mode choice was modeled as functions of variables such as sociodemographics, neighborhood crime density (as a safety measure), and walk score (as a measure of walkability). Different crime types were examined, and a crime index was introduced. Results suggest that both walk score and the crime index at the destination can be considered meaningful predictors of individuals’ mode usage. The crime index at origin, however, does not show a significant and meaningful effect.

Activity-Based Model with Dynamic Traffic Assignment and Consideration of Heterogeneous User Preferences and Reliability Valuation: Application to Toll Revenue Forecasting in Chicago, Illinois

To forecast the impact of congestion pricing schemes, it is essential to capture user responses to these schemes and the resulting dynamics of traffic flow on the network. The responses of users must include route, departure time, and mode choices. To capture the effects of these decisions, this paper lays out a framework for the integration of the relevant elements of an activity-based model (ABM) with a dynamic traffic assignment (DTA) model and a simulation framework to support the analysis and evaluation of various pricing schemes. In this paper, a multicriterion dynamic user equilibrium traffic assignment model is used; the model explicitly considers heterogeneous users who seek to minimize travel time, out-of-pocket cost, and travel time reliability in the underlying route choice framework. In addition to the methodological developments, various demand and supply parameters are estimated and calibrated for the selected application network (the Greater Chicago, Illinois, network). This paper showcases the integration of ABM components and a DTA in one coherent modeling framework for the implementation and evaluation of congestion pricing in an actual large-scale network.

From Personal Attitudes to Public Opinion: Information Diffusion in Social Networks Toward Sustainable Transportation

A model of a social network–based attitude diffusion system in the context of activity and travel choice behavior is presented. The principal mechanisms contributing to attitude formation were identified, and mathematical models to capture these processes were developed. The primary contributions of this research are (a) the modeling of attitude diffusion according to social and learning mechanisms and (b) the evolution of these attitudes over time in a lattice neighborhood social network. The agent-based framework presented is sufficiently general and flexible to allow the building of a more complete representation of information diffusion and attitude formation within activity and travel behavior choice dimensions, for example, mode choice or departure time choice. The framework allows the extension of the presented approach with additional social network structures, information sources, and social interaction mechanisms in the physical and virtual realms or the extension and modification of the presented approach to simulate the impact of information-based management strategies.

Predictive Analytics to Improve Pricing and Sourcing in Third-Party Logistics Operations

Pricing shipments and sourcing capacity for a third-party logistics (3PL) provider operating in a spot market requires real-time decision making that is ripe for computer-based support driven by analytics. A decision support system outlined here leverages the 3PL providers historical shipment data along with its knowledge of both sides of the shipping process to increase profits and to better perform the pricing and sourcing tasks. At the core of the system are discrete choice models for shippers and carriers along with a profit maximization model. The discrete choice models predict the acceptance or rejection of an offer for a shipment to shippers and a bid for capacity to carriers. The profit maximization model determines the shipper price that maximizes the 3PL providers expected profit. In addition to those models are procedures for determining a list of potential carriers for an incoming shipment and also for ranking those carriers. As its main outputs, the system produces a shipper price and a ranked carrier list. The system is applied to real-world data provided by a 3PL company, with excellent results. The system is able to produce competitive yet profitable prices and to select potential carriers that would increase the 3PL providers profits.

Integrating Behavioral Models in Network Operations: Evaluating Traveler Information and Demand Management for Weather-Related Events

The main goal of the study was to integrate demand models into weather-responsive network traffic estimation and prediction system methodologies. The study examined the behavioral responses of travelers along several dimensions in response to weather-related transportation management strategies in conjunction with active travel demand management strategies before and during severe weather events. Specific management interventions included pretrip, information-based mode, and departure time choice adjustments, as well as policy-based rescheduling of school hours. The paper presents a case study of the Chicago, Illinois, area network under snow conditions to assess the effect of a combination of demand management strategies to maintain the same level of network performance as under clear weather conditions. A combination of earlier dissemination of information and school-opening policy resulted in a similar level of network performance maintained under a median snow day as compared with a clear weather day. The paper presents integrated supply and demand models for simulation and an assessment of demand management strategies in conjunction with weather-related congestion.

Mode-Switching Behavior with the Provision of Real-Time Multimodal Traveler Information

Travelers mode choice behavior with the presence of high-quality smartphone-delivered multimodal information (SMMI) has rarely been addressed. This study investigated commuters en route decisions about switching from automobile to park-and-ride; high-quality SMMI about current level of service was provided. A stated preference survey was conducted in Shanghai, China, and the data from that survey were analyzed through a panel mixed logit model. The model accounted for the variations in individuals preferences regarding travel time and correlations between repeated choices of the same individual. Modeling results showed that SMMI had significant effects on commuters mode choice behavior and that the effects depended on gender, age, income, and education. The results revealed a high but plausible value of time (VOT). SMMI was evaluated by the users and compared with their previous experiences. This result was particularly obvious for respondents who were least likely to switch and were experienced drivers, accessed dynamic traffic information regularly, and would have been more likely to switch if an auto delay is nonrecurrent. There was also a strong inertia impact; people who were not familiar with park-and-ride would have been less likely to switch from driving to park-and-ride. Travel time was highly valued for commute trips, as revealed by the high VOT and high percentage of respondents whose major criterion for mode choice is travel time. There was a high willingness to pay for an improved service of rail transit; this willingness was reflected by the significant explanatory variable “comfort level of rail transit.”

Incorporating social media in travel and activity choice models: conceptual framework and exploratory analysis

ABSTRACT Location-based social networking data provide an important new dimension in understanding travel choice behaviour, providing high levels of location and time accuracy over long time frames in conjunction with explicit friendship network information. Such data allow examination of location choice dynamics and social networking aspects explicitly. This paper presents an exploration of social network based dynamics of choice set generation in the context of activity and travel choice behaviour, especially destination choice. Using data from an online location-based social network, the paper explores the spatiality of destinations and social network influence on travellers’ destination choice in the Chicago metropolitan area. The results show that social relationships play a role in travellers’ destination choices and that distance plays a strong role in social networks as in location choice. Connectivity through social network structure is examined jointly with individuals’ spatial activity engagement; the number of virtual friends is found to significantly influence actual physical travel behaviour. Finally, caveats in using social networking data for behaviour analysis and planning are discussed.