published journal article
published journal article
The authors propose PEXIC (Priced EXchanges in Intersection Control), a new concept and algorithm for traffic signal control that incorporates user heterogeneity on value of delay savings. The algorithm assigns phases with associated delays, taking into account the vehicle travelersâ?? values for experienced delay. Applying principles of envy-freeness, the authors develop a pricing scheme that addresses fairness by minimizing user envy via compensatory monetary transfers among users. PEXIC is Pareto efficient and budget balanced, and thus financially self-sustainable without external subsidy. The optimization is solved sequentially on a rolling horizon basis: first the phasing, and next the pricing. PEXIC achieves significant cost reductions for a large range of volumes and usersâ?? value heterogeneity levels. Inclusion of user heterogeneity also proved to be fairer than standard delay minimization that disregards individual vehiclesâ?? values for delay savings. Furthermore, the authors show that arbitrage is not possible, thus there are no incentives to drive just to collect those payments. The method used has polynomial complexity and it is suitable for real-world implementation.
conference paper
conference paper
We analyze the impact of state and local incentives on household ownership of hybrid electric vehicles (HEVs) while accounting for their socio-economic characteristics. Our large dataset combines household data from the 2009 National Household Travel Survey (NHTS) with zip code-level retail gas prices and congestion data from the Texas Transportation Institute, as well as information about state and city incentives. These incentives include unrestricted HOV access, income tax credit, sales tax rebates, and city parking privileges. By contrast, studies published so far focused on just one or two incentives and/or they analyzed only their local impact. We find that unrestricted access to HOV lanes paired with a parking incentive or sales tax rebates/exemptions can effectively promote household ownership of HEVs; by contrast, state income tax credits do not appear to have a statistically significant impact. Moreover, our results indicate that households are more likely to purchase HEVs in areas where gasoline prices are higher, which suggests that increasing gasoline tax could effectively promote HEVs. Understanding the effectiveness of various government policies is important at a time when there is renewed interest in promoting HEVs to address our dependence on foreign oil, air pollution and global warming.
published journal article
conference paper
In this study, a comprehensive framework for forecasting a time-dependent future traffic pattern is introduced. In transportation planning, the future travel demand is calculated based on the change of socio-economic variables at a target year. In the forecasting process, however, the physical capacity of road network does not be considered explicitly, so a traffic simulation model might fail to load the forecasted OD demand table. Therefore, forecasting a dynamic future traffic pattern is a very difficult task with existing methods and models. In order to overcome the obstacle, the authors develop a comprehensive traffic simulator in which the supply side and the demand side of transportation system are modeled within a unified framework. The developed model relies on an activity-based approach and the household interactions among household members are also considered. A numerical example shows the developed model can find an equilibrium point between the dynamic travel demand and network supply.
published journal article
Cities around the world vary in terms of the structure of their transportation networks and travel demand patterns. Despite these variations significantly impacting opportunities for travelers to share trips, no metrics are available to jointly characterize a region’s transportation network and travel demand in terms of the potential for travelers to share space with each other on network links, or what we call person-trip shareability. This study proposes metrics to quantify the shareability of person-trips in a city subregion, as a function of three inputs—the underlying transportation network, origin–destination (OD) travel demand, and a maximum permissible detour parameter. After defining person-trip shareability and providing principles for operationalizing this definition, we formulate a fundamental metric, called flow overlap. Flow overlap measures, for a person-trip traversing a given path, the weighted (by link distance) average number of other person-trips sharing the links along the original person-trip’s path. We then formulate the Maximum Network Flow Overlap Problem (MNFLOP), a math program that assigns person-trips to network paths that maximize network-wide flow overlap. We utilize the MNFLOP output to calculate shareability metrics at various levels of aggregation: person-trip level, OD level, origin or destination level, network level, and link level. The study applies the MNFLOP and associated shareability metrics to different OD demand scenarios in the Sioux Falls network. The computational results verify that MNFLOP (i) assigns person-trips to paths such that flow overlaps significantly increase relative to shortest path assignment, (ii) can meaningfully differentiate between different OD trip matrices in terms of flow overlap, and (iii) metrics can meaningfully quantify demand dispersion from a single network node/location considering the underlying road network. Finally, we validate MNFLOP’s ability to quantify shareability by showing that demand patterns with higher flow overlap are strongly associated with lower mileage routes for a last-mile microtransit service. The paper includes an extensive discussion of other potential future uses of the MNFLOP and its associated shareability metrics.
published journal article
published journal article
published journal article