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

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

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

Optimal Integrated Dynamic Traffic Assignment and Signal Control for Evacuation of Large Traffic Networks with Varying Threat Levels

This research contributes to the state of the art and state of the practice in solving a very important and computationally challenging problem in the areas of urban transportation systems, operations research, disaster management, and public policy. Being a very active topic of research during the past few decades, the problem of developing an efficient and practical strategy for evacuation of real-sized urban traffic networks in case of disasters from different causes, quickly enough to be employed in immediate disaster management scenarios, has been identified as one of the most challenging and yet vital problems by many researchers. More specifically, this research develops fast methods to find the optimal integrated strategy for traffic routing and traffic signal control to evacuate real-sized urban networks in the most efficient manner. In this research a solution framework is proposed, developed and tested which is capable of solving these problems in very short computational time. An efficient relaxation-based decomposition method is proposed, implemented for two evacuation integrated routing and signal control model formulations, proven to be optimal for both formulations, and verified to reduce the computational complexity of the optimal integrated routing and signal control problem. The efficiency of the proposed decomposition method is gained by reducing the integrated optimal routing and signal control problem into a relaxed optimal routing problem. This has been achieved through an insight into intersection flows in the optimal routing solution: in at least one of the optimal solutions of the routing problem, each street during each time interval only carries vehicles in at most one direction. This property, being essential to the proposed decomposition method, is called "unidirectionality" in this dissertation. The conditions under which this property exists in the optimal evacuation routing solution are identified, and the existence of unidirectionality is proven for: (1) the common Single-Destination System-Optimal Dynamic Traffic Assignment (SD-SODTA) problem, with the objective to minimize the total time spent in the threat area; and, (2) for the single-destination evacuation problem with varying threat levels, with traffic models that have no spatial queue propagation. The proposed decomposition method has been implemented in compliance with two widely-accepted traffic flow models, the Cell Transmission Model (CTM) and the Point Queue (PQ) model. In each case, the decomposition method finds the optimal solution for the integrated routing and signal control problem. Both traffic models have been coded and applied to a realistic real-size evacuation scenario with promising results. One important feature that is explored is the incorporation of evacuation safety aspects in the optimization model. An index of the threat level is associated with each link that reflects the adverse effects of traveling in a given threat zone on the safety and health of evacuees during the process of evacuation. The optimization problem is then formulated to minimize the total exposure of evacuees to the threat. A hypothetical large-scale chlorine gas spill in a high populated urban area (downtown Tucson, Arizona) has been modeled for testing the evacuation models where the network has varying threat levels. In addition to the proposed decomposition method, an efficient network-flow solution algorithm is also proposed to find the optimal routing of traffic in networks with several threat zones, where the threat levels may be non-uniform across different zones. The proposed method can be categorized in the class of "negative cycle canceling" algorithms for solving minimum cost flow problems. The unique feature in the proposed algorithm is introducing a multi-source shortest path calculation which enables the efficient detection and cancellation of negative cycles. The proposed method is proven to find the optimal solution, and it is also applied to and verified for a mid-size test network scenario

Evaluation of bike accessibility in an urban network

Encouraging active and sustainable modes of transport has been an important goal for all transport authorities in developed countries. In many cities, cycling as an active transport mode is only directly investigated within the limited scope of separate road development projects. Efficient moves towards urban transport networks that favour sustainable modes can only be possible by accurate, realistic, and robust evaluation techniques to measure existing facilities, and to assess future network development scenarios. As a result, there is a need for tools and techniques to generate a comprehensive network perspective with regards to cycling facilities. This paper aims to introduce a method to evaluate bike accessibility between given origins and destinations. Considering an urban trip all the way from an origin (O) to a destination (D), the proposed evaluation method is capable of incorporating the key concerns of cyclists by applying route choice coefficients of a cycling trip into a path generation process. Moreover, the proposed method takes into account multiple route options available to ride between an origin-destination (OD) pair. The method is applied to the network of Brisbane, Australia. The network includes all levels of road hierarchy suitable for bikes (arterials, collectors, and access roads) and covers the effect of available bike facilities on road (bike paths, bike lanes, wide curb side lanes, and general traffic lanes). Indicative results are provided on bike accessibility to the Central Business District (CBD) from the suburbs

A study on the utilization of Park-and-Ride lots in South East Queensland

This paper describes the utilization of park-and-ride (PNR) lots in South East Queensland (SEQ). In the literature, utilization of PNR lots is generally taken as a static property. We consider utilization as a dynamic property of PNR lots and hence study utilization by conducting a survey of PNR lots for a period of time during a weekday morning. From the survey we collected data on the number of car arrivals and their arrival time. Utilization is measured as the ratio of usage (number of cars parked) of the PNR lot to its capacity. Also, information on the informal on-street parking was gathered wherever possible. We designed and carried out the survey in 20 PNR lots of SEQ. The PNR lot survey began a few minutes prior to the arrival of the first public transport service near the PNR lot and ended when either the lot was fully occupied or there were very infrequent arrivals. For each arriving car, its arrival time within a 5-min interval was recorded. Results suggest that most of the PNR lots are filled or reach a steady arrival pattern by 9:30 am

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

Value of demand information in autonomous mobility-on-demand systems

© 2019 Elsevier Ltd Effective management of demand information is a critical factor in the successful operation of autonomous mobility-on-demand (AMoD) systems. This paper classifies, measures and evaluates the demand information for an AMoD system. First, the paper studies demand information at both individual and aggregate levels and measures two critical attributes: dynamism and granularity. We identify the trade-offs between both attributes during the data collection and information inference processes and discuss the compatibility of the AMoD dispatching algorithms with different types of information. Second, the paper assesses the value of demand information through agent-based simulation experiments with the actual road network and travel demand in a major European city, where we assume a single operator monopolizes the AMoD service in the case study area but competes with other transportation modes. The performance of the AMoD system is evaluated from the perspectives of travelers, AMoD operators, and transportation authority in terms of the overall system performance. The paper tests multiple scenarios, combining different information levels, information dynamism, and information granularity, as well as various fleet sizes. Results show that aggregate demand information leads to more served requests, shorter wait time and higher profit through effective rebalancing, especially when supply is high and demand information is spatially granular. Individual demand information from in-advance requests also improves the system performance, the degree of which depends on the spatial disparity of requests and their coupled service priority. By designing hailing policies accordingly, the operator is able to maximize the potential benefits. The paper concludes that the strategic trade-offs of demand information need to be made regarding the information level, information dynamism, and information granularity. It also offers a broader discussion on the benefits and costs of demand information for key stakeholders including the users, the operator, and the society