Our team at the a ITSLab together with the TrancikLab and the University of Massachusetts at Amherst (UMass) 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 relies on an app-based travel incentive tool designed to influence users’ travel choices by offering them real-time information and rewards. Our lab is using an open-source simulation platform, SimMobility , and an energy model, TripEnergy, to test Tripod. The system model, which simulates the Greater Boston area, is be able to dynamically measure Tripod’s energy impact as changes to the network and travelers’ behavior occur.
Tripod presents users with personalized options via a smartphone app, and includes a reward points system to incentivize users to adopt energy-efficient travel options. In the future, reward points, or tokens, could be redeemed for prizes or discounts at participating vendors, or could be transferred amongst users in a social networks.

The project is funded by the US Department of Energy Advanced Research Projects Agency-Energy (ARPA-E). In a recent article on Nexus Media about the agency the Tripod project was highlighted. Check it out!

We are observing a disruption in the urban transportation worldwide. The number of cities offering shared-use on-demand mobility services is increasing rapidly. They promise sustainable and affordable personal mobility without a burden of owning a vehicle. Despite growing popularity, on-demand services still have a hard time dealing with heterogeneous demand patterns. It’s the case of rebalancing of one-way carsharing and Uber/Lyft like services. In the past few years we have been studying Autonomous Mobility on Demand (AMOD) solutions: an on-demand self-driving electric vehicle service. In a study with ARES group at MIT and SMART, we built upon our simulation platform, SimMobility, to study the operation of AMOD systems and its impact on travel decision making. We compare the performance of different AMOD fleet sizes, parking lot locations and operational (rebalancing) algorithms and uncovered changes in the overall multi-modal transportation system and in individual mobility patterns, specifically in regard to modal shares, routes, and destinations.

[2016] “Simulation Framework for Rebalancing of Autonomous Mobility on-Demand Systems”
Marczuk, M., Soh, H., Lima Azevedo, C., Lee, D.H., Frazzoli, E. 5th International Conference on Transportation and Traffic Engineering (ICTTE 2016), Lucerne, Switzerland, July 6-10, 2016

[2016] “Microsimulation of Demand and Supply of Autonomous Mobility On-Demand”
Lima Azevedo, C., Marczuk, K., Raveau, S., Soh, H., Adnan, M., Basak, K., Loganathan, H., Deshmunkh, N., Lee, D. H., Frazzoli, E., Ben-Akiva, M. E. Transportation Research Record: Journal of the Transportation Research Board, No. 2564., pp. 21-30.

(A day of AMOD operation in Singapore’s extended CBD network; pink are vehicles in service – from station to pick-up, occupied, from drop-off to station; blue are vehicles being rebalanced; Credits to Kasia and Harold)

The development and calibration of complex traffic models demands parsimonious techniques, because such models often involve hundreds of thousands of unknown parameters. The Weighted Simultaneous Perturbation Stochastic Approximation (W–SPSA) algorithm has been proven more efficient than its predecessor SPSA, particularly in situations where the correlation structure of the variables is not homogeneous. This is crucial in traffic simulation models where effectively some variables (e.g. readings from certain sensors) are strongly correlated, both in time and space, with some other variables (e.g. certain OD flows). In situations with reasonably sized traffic networks, the difference is relevant considering computational constraints. However, W–SPSA relies on determining a proper weight matrix (W) that represents those correlations, and such a process has been so far an open problem, and only heuristic approaches to obtain it have been considered.
In this seminar, W–SPSA is presented in a formally comprehensive way, where effectively SPSA becomes an instance of W–SPSA, and explores alternative approaches for determining the matrix W. It is demonstrated that, relying on a few simplifications that marginally affect the final solution, W matrices that considerably outperform SPSA can be obtained. The performance of the proposed algorithm is presented in two applications in motorway networks in Singapore and Portugal, using a dynamic traffic assignment model and a microscopic traffic simulator, respectively.

[2015] “W–SPSA in practice: Approximation of weight matrices and calibration of traffic simulation models”
Antoniou, C., Lima Azevedo, C., Lu, L., Pereira, F., Ben-Akiva, M. E. Transportation Research Part C, Volume 59, Pages 129–146.[pre-print]