Antoine Petit’s research while affiliated with University of Illinois Urbana-Champaign and other places

This paper presents design methods for a flexible-route transit system, in which vehicles travel within predetermined areas to provide door-to-door service. The main advantage of this system is that passengers no longer have to access transit stations in order to gain service. This system is suitable for low and heterogeneous passenger demand distribution as it features a hybrid system layout that includes both a hub-and-spoke network in the peripheral region and a grid network in the central region, along with heterogeneous local routes that address local demand variations. Continuum approximation (C.A.) is used to reduce the computation burden by formulating the design problem with respect to a few decision variables. We compare the performance of the proposed transit system with (i) the typical fixed-route system, (ii) the homogeneous flexible-route grid system, and (iii) the flexible-route grid system with local routes, in hypothetical settings. It is found through our numerical examples that the integration of the three proposed features (i.e. flexible transit, local tubes, and hybrid structure), as compared to counterparts with only two or fewer features, yields lower combined agency and user costs under the assumed low heterogeneous demand distribution. We then apply the design framework to a more realistic case for the City of Changzhi, China. An implementable design for Changzhi is developed, and its performance verified with simulations, demonstrating accuracy and applicability of the proposed continuum approximation model.

This paper proposes an integrated methodological framework to design a spatially heterogeneous bus route network and time-dependent service headways to serve travel demand that varies over time and space. Travelers choose to use either the transit mode or the driving mode (as well as travel paths in the corresponding modal network) that minimizes their equilibrium travel cost. In addition, transit routes involve dedicated bus lanes that occupy part of the city streets and affect the capacity reserved for private cars. Hence, roadway congestion depends on the transit route design, and its dynamic evolution is described by regional macroscopic fundamental diagrams. The proposed modeling framework consists of two parts: a bus network optimization module based on continuum approximation that produces optimal headways and local route spacing, and a dynamic aggregated network model that determines route choice, mode split, and user equilibrium conditions. An iterative solution algorithm is developed to solve the integrated model. Numerical experiments are used to demonstrate the applicability of the proposed modeling framework, and to conduct a careful analysis on the influence of the demand pattern on the transit network design, roadway congestion, and the overall system performance.

Traditional bus bunching control methods (e.g., adding slack to schedules, adapting cruising speed), in one way or another, trade commercial speed for better system stability and, as a result, may impose the burden of additional travel time on passengers. Recently, a dynamic bus substitution strategy, where standby buses are dispatched to take over service from late/early buses, was proposed as an attempt to enhance system reliability without sacrificing too much passenger experience. This paper further studies this substitution strategy in the context of multiple bus lines under either time-independent or time-varying settings. In the latter scenario, the fleet of standby buses can be dynamically utilized to save on opportunity costs. We model the agency’s substitution decisions and retired bus repositioning decisions as a stochastic dynamic program so as to obtain the optimal policy that minimizes the system-wide costs. Numerical results show that the dynamic substitution strategy can benefit from the “economies of scale” by pooling the standby fleet across lines, and there are also benefits from dynamic fleet management when transit demand varies over time. Numerical examples are presented to illustrate the applicability and advantage of the proposed strategy. The substitution strategy not only holds the promise to outperform traditional holding methods in terms of reducing passenger costs, they also can be used to complement other methods to better control very unstable systems.

Bus headways are typically susceptible to external disturbances (e.g., due to traffic congestion, clustered passenger arrivals, and special passenger needs), which create gaps in the system that grow eventually into bunching. Although many control strategies, such as static and dynamic holding strategies, have been implemented to mitigate the effects of unreliable bus schedules, most of them would impose longer dwell times on the passengers. In this paper, we investigate the potential of an alternative bus substitution strategy that is currently implemented by some transit agencies in an ad-hoc manner. In this strategy, the agency deploys a fleet of standby buses to take over service from any early or late buses so as to contain deviations from schedule, and the intention is to impose minimum penalties on the onboard passengers. We develop a discrete-time infinite-horizon approximate dynamic programming approach to find the optimal policy to minimize the overall agency and passenger costs. It is shown through numerical examples that schedule deviations can be controlled by regularly inserting standby buses as substitutions. In some implementation scenarios, the proposed strategy holds the potential to achieve comparable performance with some of the most advanced strategies, and to outperform the conventional slack-based schedule control scheme. In light of the emerging opportunities associated with autonomous driving, the performance of the proposed strategy can become even stronger due to the reduction in costs for keeping the fleet of standby buses.

Accurate uncertainty modelling of social activities is impossible using available geographic information data and typical geographic information system algorithms due to the Uncertain Geographic Context Problem (UGCoP). UGCoP worsens when data as vague or obsolete, competing social models are available, and parameter values are not fully known. This research reduces UGCoP by explicitly representing uncertainty in input data, algorithms, and visualization tools using Monte Carlo methods. Rich contextual social information is retained by storing dozens of demographic attributes from surveys and censuses of all people in the study area. To include this detail requires large-scale modeling involving demographic and land use forecasting models, agent-based models, transportation dynamic models, and other computationally complex operations necessitating parallel algorithms and distributed computing systems. Named the Framework for Incorporating Complex Uncertainty Systems, it includes multiple free and open-source software tools, especially the Object Modeling System, to allow easy inclusion of additional models written in multiple third and fourth generation programming languages. This research presents the space-time uncertainty quantified modeling environment, multiple model components, and web browser visualization tools necessary to inspect all data and results of this extendable social and infrastructure system of systems analysis approach. This research will be demonstrated with a case study in the Philippines supporting risk analysis.

This research effort is developing a computational framework to support federated models of complex urban systems and enable information support for planning and response in emergency management. Systems analysis has been advocated to support emergency management activities, and there are a number of individual domain models designed to represent various system elements. However, effective implementation of this approach has its challenges. Traditional system analysis is often performed at regional or country scales. Further, information collection tends to be reductionist in process focusing on mission before the operating environment. Thus, there is limited data available to support high resolution urban systems modeling beyond localized areas. However, dense urban environment complexity requires the ability to capture and integrate the interrelationships between subpopulations and infrastructural systems. This system of systems modeling approach supports the analysis of cascading effects through interdependent infrastructure networks and the anticipated impacts on the subpopulations it supports, such as ethnicity, social class, access to transportation, or previously available services. The results are expected to reduce analyst workload by generating geospatial products and systems perspectives of demographic and infrastructure characteristics. We will be presenting an integrated infrastructure system demonstrating the cascading effects of component failure(s) combined with the effects on neighborhood-scale populations. The results are delivered to end-users using a geospatial visualization tool that includes information about the quality of the data products and the ability of the data to support information critical to emergency planning and response.