The paper presents the system optimization (SO) framework of Tripod, an integrated bi-level transportation management system aimed at maximizing energy savings of the multi-modal transportation system. From the user’s perspective, Tripod is a smartphone app, accessed before performing trips. The app proposes a series of alternatives, consisting of a combination of departure time, mode, and route. Each alternative is rewarded with an amount of tokens which the user can later redeem for goods or services. The role of SO is to compute the optimized set of tokens associated with the available alternatives to minimize the system-wide energy consumption under a limited token budget. To do so, the alternatives that guarantee the largest energy reduction must be rewarded with more tokens. SO is multi-modal, in that it considers private cars, public transit, walking, car pooling, and so forth. Moreover, it is dynamic, predictive, and personalized: the same alternative is rewarded differently, depending on the current and the predicted future condition of the network and on the individual profile. The paper presents a method to solve this complex optimization problem and describe the system architecture, the multi-modal simulation-based optimization model, and the heuristic method for the online computation of the optimized token allocation. Finally it showcases the framework with simulation results.

Increasing commute distances often lead to increased auto-dependency and is a major problem in many developed as well as developing countries. While in developed countries, the propensity to commute long distances generally originates from the preference to work in the core of the city and live in the suburb or periphery, in developing countries, the trend is often quite the opposite. For example, in Bangladesh, people generally have a strong preference to live at the heart of the major cities even if they work at the peripheral areas of the city, in another city or in a rural area. Further, it is also not uncommon to maintain split-families where the earning member of the family lives near the workplace while the rest of the family is based in a big city (subject to affordability). These phenomena lead to substantial increase in Vehicle Miles Travelled (VMT) and add burden to the transport infrastructure.The focus of the research is to explore the key factors that induce middle and upper-middle class commuters in Bangladesh to live away from their workplace and/or maintain splitfamilies. A case study is conducted using Stated Preference (SP) surveys conducted among the faculty members of two universities: one located at the periphery of the capital city and the other quite far away. Discrete Choice Models are developed using the collected data. Results reveal that albeit some differences, for both cases, the choices are strongly driven by quality of the education institutes and the house rent. Factors like gender, income and car-ownership, which traditionally play a strong role in the context of developed countries, are found to be of less significance.The models, though estimated with limited data, provide useful insights about the factors that drive residential location choices in the context of a developing country and can help in formulating policies for encouraging people to live closer to their workplaces and thereby reduce commuter VMT.

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