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.
This project aims at developing an integrated approach for future freight and logistics surveys including all relevant freight entities: establishments (including logistics operators and 3PL), carriers/drivers/vehicles, and shipments. The proposed approach leverages a coherent and holistic survey methodology and fully integrated survey instruments. It is based on innovative and scalable technologies with considerable time and geographical coverage (national, regional/urban and rural areas). The framework aims to ameliorate inherent limitations in current freight data collection methods, obtain unprecedented freight data for statistical purposes, and enable the implementation of a new generation of freight models, including agent-based models.
FMS is a smartphone and prompted-recall-based integrated activity-travel survey. It uses a smartphone app and an online prompted recall survey to collect demographic and travel data from participants. Data from the app are 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.
The goal of this project is to develop an agent-based framework for urban-level analyses and predictions of passenger responses to possible de-carbonization policies. This framework incorporates behavioral preferences regarding emerging vehicle technologies and mobility services. Prevailing attributes and typologies that describe mobility, energy and emissions characteristics of cities worldwide are being determined. The typologies are being used to construct prototypical cities to evaluate in predicting the outcomes of policy options.
SimMobility is the simulation platform that serves as the nexus of Future Mobility research evaluations. It integrates mobility-sensitive behavioral models with state-of-the-art simulators to predict the impact of mobility demands on transportation networks, intelligent transportation services and vehicular emissions. Analyses of technology, policy and investment alternative scenarios is supported. SimMobility models millions of agents in simulations that are scalable from seconds to years and from neighborhoods to countries.
The team of ITSLab, TrancikLab (MIT) and UMass-Amherst is developing the Tripod system – ‘Sustainable Travel Incentives with Prediction, Optimization and Personalization’ – to incentivize 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. Tokens can be redeemed or transferred.
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.