Understanding and modeling behavior is essential for urban planning and project evaluation. It is necessary to identify, quantify and model all the relevant factors influencing individual behavior and decision-making processes. These decisions can be on a long term scale (e.g. household and firms location), a medium term scale (e.g. day-to-day activity patterns) or a short term scale (e.g. travel alternatives for particular trips). This project seeks to develop state-of-the-art behavioral models in the context of agent-based simulation.
This research intends to develop models and methodologies for toll setting using dynamic pricing for managed lanes and based on real time prediction of demand and traffic conditions. Appropriate route choice models will be considered in order to estimate the demand for toll and free lanes. The estimation of traffic conditions will be carried out using a simulator. A real-time optimization model will be developed with the choice model and simulator embedded in order to maintain the dynamic prediction of demand and traffic conditions.
DynaMIT (Dynamic Network Assignment for the Management of Information to Travelers) is a multi-modal multi-data source driven, simulation-based network performance prediction system. Its main features include 1) the modeling of multiple modes including public transit, mobility-on-demand services such as Uber/Lyft, car and ride sharing, etc.; 2) Online calibration of demand and supply parameters; 3) context mining and the scenario analyzer for unstructured data; 4) The strategy optimization module for network control strategy optimization.
The goal of this project is to develop a viable framework for analyses and predictions of passenger responses to possible future de-carbonization policies at the urban level. This framework will incorporate behavioral preferences with regard to emerging and upcoming vehicle technologies and mobility services. These will be realized via extensions to an existing agent-based simulation model for urban networks. To test its applicability to many different urban scenarios that exist at the global level, we will determine prevailing city attributes and typologies that describe mobility, energy and emissions characteristics world-wide. From these typologies, we will develop prototypical cities based on existing and simulated data. We will then perform scenario analyses to predict outcomes of potential policy options.
FMS is a smartphone and prompted-recall-based integrated activity-travel survey. It uses a combination of a smartphone app, available for Android and iOS, and an online prompted recall survey to collect both demographic and travel data from participants. Data collected from the smartphone app is uploaded to a central server, mapped, analyzed, and made accessible to the participant from the project website, where he or she is asked to provide detailed travel information via a prompted-recall survey. The detailed, accurate data collected by FMS can be used for transportation modeling and urban planning.
SimMoblity is the simulation platform of the Future Urban Mobility Research Group at the Singapore-MIT Alliance for Research and Technology (SMART) that aims to serve as the nexus of Future Mobility research evaluations. It integrates various mobility-sensitive behavioral models with state-of-the-art scalable simulators to predict the impact of mobility demands on transportation networks, intelligent transportation services and vehicular emissions. The platform enables us to simulate the effects of a portfolio of technology, policy and investment options under alternative future scenarios. SimMobility encompasses the modeling of millions of agents, from pedestrians to drivers, from phones and traffic lights to GPS, from cars to buses and trains, from second-by-second to year-by-year simulations, across entire countries.
Our team at the a ITSLab together with the TrancikLab (MIT) and the University of Massachusetts at Amherst are developing the Tripod system – ‘Sustainable Travel Incentives with Prediction, Optimization and Personalization’ – a system that incentivizes travelers to pursue specific routes, modes of travel, departure times, ride sharing, trip making and driving styles in order to reduce energy use. Tripod presents users with personalized options via a smartphone app, and includes real-time information and rewards (tokens) to incentivize users to adopt energy-efficient travel options at the system-wide level. Tokens can be redeemed for prizes or discounts at participating vendors, or can be transferred amongst users in a social networks.
This research identified key factors that affect heavy truck route choices. We evaluated the choice among free and toll road alternatives and used discrete choice models to predict the routing decisions. These models have helped us gain insight into the route choice scenarios faced during truck trips, and they may suggest new tolling strategies and products. The model will be used to evaluate the potential of these products to increase the toll road market share for various truck trip segments.
GPS-enabled devices, such as smart phones and GPS loggers are gaining popularity as tools to collect individual travel behavior data in travel surveys. In this project, we propose to develop a customized adaptive stated preferences survey system that extends the capability of existing GPS-enabled travel surveys. An event-based preferences experiment environment will be setup that allows hypothetical questions to be asked based on observed user travel and activity behavior. The objective of this project is to develop a methodology to study consumer preferences for mobility solutions based on behavior observed through GPS-enabled devices and prompted recall surveys.
Understanding and incorporating measures of travel well-being in transportation research is critical for the design and evaluation of policies aimed at enhancing well-being. Individual well-being is derived not only from what a person actually does or is, but also from their capabilities (i.e. feasible functioning forms that they could have achieved or could have been). In this study we conduct surveys and measure mobility patterns and well-being using smartphone technology (enabled with GPS, GSM, Wi-Fi and accelerometer). This technology overcomes the limitations of conventional survey methods, and we aim to develop novel measures of well-being based on travelers' mobility potential (i.e. from motility) as well as compare real-time and retrospective well-being measures.