Neema Nassir

Intermodal Path Algorithm for Time-Dependent Auto Network and Scheduled Transit Service

A simple but efficient algorithm is proposed for finding the optimal path in an intermodal urban transportation network. The network is a general transportation network with multiple modes (auto, bus, rail, walk, etc.) divided into the two major categories of private and public, with proper transfer constraints. The goal was to find the optimal path according to the generalized cost, including private-side travel cost, public-side travel cost, and transfer cost. A detailed network model of transfers between modes was used to improve the accounting of travel times during these transfers. The intermodal path algorithm was a sequential application of specific cases of transit and auto shortest paths and resulted in the optimal intermodal path, with the optimal park-and-ride location for transferring from private to public modes. The computational complexity of the algorithm was shown to be a significant improvement over existing algorithms. The algorithm was applied to a real network within a dynamic traffic and transit assignment procedure and integrated with a sequential activity choice model.

Algorithm for Intermodal Optimal Multidestination Tour with Dynamic Travel Times

This paper presents an efficient algorithm that finds the intermodal optimal tour (origin to origin) in a time-dependent transportation network while the algorithm implicitly solves the park-and-ride facility choice problem with the inherent park-and-ride constraints for a traveler with a sequence of destinations to visit. To solve the problem, a network expansion technique that captures the constraints of park-and-ride behavior in the model and that transforms the park-and-ride choice problem into a dynamic network flow problem is introduced. An efficient iterative labeling algorithm that finds the optimal intermodal tour to serve the sequence of activities is also introduced. Multisource shortest-path runs are used in the iterative labeling algorithm to find the optimal tour with several intermediate destinations in an efficient manner. The performance of the algorithm is compared with the performance of existing approaches, and improvement is indicated. The solution method proposed benefits from the advantages of Dijkstras shortest-path algorithm, which is made possible by (a) a nontrivial transformation of the original problem into a dynamic network flow problem and (b) an innovative use of a multisource shortest path in the context of origin–destination choice. The solution algorithm integrates time-dependent auto and transit shortest-path algorithms to find the optimal tour. The algorithm is implemented, coded, and tested on a real network, and the results are promising.

Choice Set Generation Algorithm Suitable for Measuring Route Choice Accessibility

A new algorithm that generated a set of paths between a pair of origin– destination nodes in a transportation network for the purpose of generating a measure of accessibility on the level of route choice was designed, developed, and tested. The proposed algorithm incorporated the well-known issue of path overlap in the process of generating the path choice set. This algorithm fit naturally into the class of iterative penalty-based Kth-shortest-path algorithms; in this class the link penalty terms are designed to reflect the amount of overlap between the paths already generated. With the proposed algorithm, paths were generated in order of decreasing utility and corrected by a path size correction factor; it was thus highly efficient in the sense that a comparatively small number of paths could result in a broad spectrum of desirable choices. The algorithm was developed in response to the Valencia paradox, which arose from using logsums from the existing algorithm for choice set generation as a route-level accessibility measure for the bicycle network in San Francisco, California. The Valencia paradox occurs when an accessibility measure decreases following an improvement to actual network accessibility. A detailed case study demonstrated the effectiveness of the proposed algorithm in minimizing this kind of paradoxical result and generating a route-level accessibility measure suitable for making fine-grained planning decisions.

Network Flow Solution Method for Optimal Evacuation Traffic Routing and Signal Control with Nonuniform Threat

An efficient two-stage network flow approach is proposed for the determination of optimal scenarios for integrated traffic routing and signal timing in the evacuation of real-sized urban networks with several threat zones, where the threat levels may be nonuniform across zones. The objective is to minimize total exposure to the threat (severity multiplied by duration) for all evacuees during the evacuation. In the problem formulation, traffic flow dynamics are based on the well-known point queue model in a time-expanded network representation. The proposed solution approach is adapted from a general relaxation-based decomposition method in a network flow formulation. The decomposition method is developed on the basis of insights into the optimal flow of traffic at intersections in the solution of the evacuation routing problem. As for efficiency, the computation time associated with the decomposition method for solving the integrated optimal routing and signal control problem is equivalent to the time required for solving the same optimal routing problem (without optimizing the intersection control plan) because the computation time required for determining the optimal signal control is negligible. The proposed solution method proves to be optimal. The method is implemented and applied to a real-sized evacuation scenario in the transportation network of Tucson, Arizona. The method is stress-tested with some inflated demand scenarios, and computation aspects are reported.

Modeling Transit Passenger Choices of Access Stop

A logit discrete choice model is developed to investigate the choice of transit access stop (i.e., departure stop). The model incorporates different components of the transit service between given origin–destination pairs at given times. A choice set generation algorithm is developed to create the set of access stop choices and calculate the time-dependent impedances from each departure stop to the destination. The correlation between the choices is treated at two levels: (a) the mode of travel and (b) the route of travel. A nested logit model structure is adopted to account for the dependencies among the choices on the same mode, and a correction term is proposed that captures the correlation between the stop choices that results from the commonality of the routes that serve the destination. The data are from the household travel survey of 2009 in Southeast Queensland, Australia and include travel records on three public transit modes: bus, train, and ferry. The case study analysis of Southeast Queensland shows the effectiveness of the proposed correction by demonstrating improvements in modeling the choice of access stop. The research concludes a new finding that the choice of access stop is affected not only by the attributes of the transit path between the journey ends but also, significantly and directly, by the attributes of the departure stop itself.

Efficient Negative Cycle–Canceling Algorithm for Finding the Optimal Traffic Routing for Network Evacuation with Nonuniform Threats

A new network flow solution method is designed to determine optimal traffic routing efficiently for the evacuation of networks with several threat zones and with nonuniform threat levels across zones. The objective is to minimize total exposure (as duration and severity) to the threat for all evacuees during the evacuation. The problem is formulated as a minimum cost dynamic flow problem coupled with traffic dynamic constraints. The traffic flow dynamic constraints are enforced by the well-known point queue and spatial queue models in a time-expanded network presentation. The key to the efficiency of the proposed method is that, for any feasible solution, the algorithm can find and can cancel multiple negative cycles (including the cycle with the largest negative cost) with a single shortest path calculation made possible by applying a proposed transformation to the original problem. A cost transformation function and a multisource shortest path algorithm are proposed to facilitate the efficient detection and cancelation of negative cycles. Zone by zone, negative cycles are canceled at the border links of the zones. The solution method is proved to be optimal. The algorithm is implemented, tested, and verified to be optimal for a midsized example problem.

Statistical Inference of Transit Passenger Boarding Strategies from Farecard Data

This research considers how one might deduce the set of attractive routes for public transit passengers, as part of a boarding strategy, from passengers’ daily choices of which path to take. From the set of attractive routes (attractive set), a public transit passenger may be assumed to board the first service that arrives at the origin stop. To determine the attractive set, a statistical inference algorithm was developed and tested using a public transit farecard data set. The proposed algorithm was developed from an exact method that investigates the distribution of repeated boarding transactions in a farecard data set and infers the so-called steady-state strategies over the observation period. The advantage of the proposed method is in identifying and eliminating occasional and tried-but-rejected path alternatives recorded during the study period. The method was tested in a case study using 6 months of farecard transactions of regular passengers for multiple major origin–destination pairs in the transit network of Brisbane, Australia. Some behavioral aspects of passengers’ attractive routes are also reported and discussed.

Park-and-Ride Lot Choice Model Using Random Utility Maximization and Random Regret Minimization

This research aims to understand the park-and-ride (PNR) lot choice behaviour of users i.e., why PNR user choose one PNR lot versus another. Multinomial logit models are developed, the first based on the random utility maximization (RUM) concept where users are assumed to choose alternatives that have maximum utility, and the second based on the random regret minimization (RRM) concept where users are assumed to make decisions such that they minimize the regret in comparison to other foregone alternatives. A PNR trip is completed in two networks, the auto network and the transit network. The travel time of users for both the auto network and the transit network are used to create variables in the model. For the auto network, travel time is obtained using information from the strategic transport network using EMME/4 software, whereas travel time for the transit network is calculated using Google’s general transit feed specification data using a backward time-dependent shortest path algorithm. The involvement of two different networks in a PNR trip causes a trade-off relation within the PNR lot choice mechanism, and it is anticipated that an RRM model that captures this compromise effect may outperform typical RUM models. We use two forms of RRM models; the classical RRM and µRRM. Our results not only confirm a decade-old understanding that the RRM model may be an alternative concept to model transport choices, but also strengthen this understanding by exploring differences between two models in terms of model fit and out-of-sample predictive abilities. Further, our work is one of the few that estimates an RRM model on revealed preference data.

Modeling Transit Users Stop Choice Behavior: Do Travelers Strategize?

Transit choice research focuses predominantly on mode choice and route choice, whereas very few studies on stop choice are conducted. To fill this gap, this research aimed to study transit stop choice behavior with a focus on how people strategize when making their choices. It is hypothesized that travelers treat stops differently based on various schemes (strategies); minimizing travel time, access time, and the number of transfers are the schemes considered in this study, and the effectiveness of several discrete choice model specifications was examined. The study found that path attributes and stop attributes have significant impacts on stop selection behavior. Furthermore, users’ socioeconomic characteristics along with trip timing play important roles in choosing transit stops. The outcomes of this study could facilitate the recent move toward development of behavioral route choice models using smart card data, which can then assist travel demand estimation models with a focus on public transport.

Estimation of Passengers Left Behind by Trains in High-Frequency Transit Service Operating Near Capacity

Measuring rail system crowding is important to transit agencies. Crowding data has implications for safety, operations control, service planning, performance measurement, and customer information. This paper proposes a bi-level regression model that transit agencies can use to estimate the number of passengers left behind on a platform by high-frequency trains operating at capacity. Inputs to the model include the number of passenger arrivals between trains and train departure times, which are derived from automatic fare collection and vehicle location data. The data are used to calculate the proposed measure of cumulative capacity shortage, which is shown to have high correlation with the number of passengers left behind. A bi-level regression approach is introduced and applied to calibrate the model parameters based on manual counts of passengers left behind. A case study using data from the Chicago Transit Authority’s Blue Line demonstrates promising results, with an adjusted coefficient of determination of 0.81. The model could be used for post-hoc analysis of crowding performance or, in the context of real-time operations monitoring, for near-term predictions of passengers left behind.