Redesigning Large-Scale Multimodal Transit Networks with Shared Autonomous Mobility Services

Public transit systems have faced challenges and opportunities from emerging Shared Autonomous Mobility Services (SAMS). This study addresses a city-scale multimodal transit network design problem, with shared autonomous vehicles as both transit feeders and a direct interzonal mode. The framework captures spatial demand and modal characteristics, considers intermodal transfers and express services, determines transit infrastructure investment and path flows, and designs transit routes. A system-optimal multimodal transit network is designed with minimum total door-to-door generalized costs of users and operators, while satisfying existing transit origin-destination demand within a pre-set infrastructure budget. Firstly, the geography, demand, and modes in each clustered zone are characterized with continuous approximation. Afterward, the decisions of network link investment and multimodal path flows in zonal connection optimization are formulated as a minimum-cost multi-commodity network flow (MCNF) problem and solved efficiently with a mixed-integer linear programming (MILP) solver. Subsequently, the route generation problem is solved by expanding the MCNF formulation to minimize intramodal transfers. To demonstrate the framework efficiency, this study uses transit demand from the Chicago metropolitan area to redesign a multimodal transit network. The computational results present savings in travelers' journey time and operators' costs, demonstrating the potential benefits of collaboration between multimodal transit systems and SAMS.Comment: 44 pages, 15 figures, under review for the 25th International Symposium on Transportation and Traffic Theory (ISTTT25

Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event

Implementation and evaluation of weather-responsive traffic management strategies

This study presents the development and application of methodologies to support weather-responsive traffic management (WRTM) strategies by building on traffic estimation and prediction system models. First, a systematic framework for implementing and evaluating WRTM strategies under severe weather conditions is developed. This framework includes activities for planning, preparing, and deploying WRTM strategies in three different time frames: long-term strategic planning, short-term tactical planning, and real-time traffic management center operations. Next, the evaluation of various strategies is demonstrated with locally calibrated network simulation-assignment model capabilities, and special-purpose key performance indicators are introduced. Three types of WRTM strategies [demand management, advisory and control variable message signs (VMSs), and incident management VMSs] are applied to multiple major U.S. areas, namely, Chicago, Illinois; Salt Lake City, Utah; and the Long Island area in New York. The analysis results illustrate the benefits of WRTM under inclement weather conditions and emphasize the importance of incorporating a predictive capability into selecting and deploying WRTM strategies

İstanbul’da Ulaştırma Sisteminin Sürdürülebilirliğinin Değerlendirilmesi

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 200

Exploring trade-offs in frequency allocation in a transit network using bus route patterns: Methodology and application to large-scale urban systems

Transit agencies seek to allocate their limited operational budget and fleet optimally to service routes in order to maximize user benefits. The Transit Network Frequency Setting Problem formulation developed in this study effectively captures the coupling between the routes and their prevailing patterns, which may have different subsets of stops visited at different times of the day. The number of riders is elastic to the prevailing number of bus trips at a given stop, which is the combination of different pattern dispatch frequencies. As a result, the study bridges the gap between the operator’s perspective where the decision unit is the pattern schedule, and the user’s perspective, which perceives frequencies at the route level. Two main formulations are introduced. The first one maximizes the number of riders and the total waiting time savings under budget, fleet, policy headway and bus loading constraints; the second minimizes the net cost under fleet, policy headway, bus loading, minimum ridership and minimum waiting time savings constraints. In both formulations, pattern headways are the decision variables. Spatial and temporal heterogeneity of ridership elasticity with respect to headway is captured. The formulations are applied to a large-scale test network for the Chicago area. The results show that a win–win solution is possible where both ridership and waiting time savings are increased, while the net cost is decreased

Gap-based transit assignment algorithm with vehicle capacity constraints: Simulation-based implementation and large-scale application

This paper presents a gap-based solution method for the time-dependent transit assignment problem with vehicle capacity constraints. A two-level, simulation-based methodology is proposed, which finds the least cost hyperpaths at the upper level and performs the assignment of transit travelers on the hyperpaths at the lower level. The detailed simulation of travelers and vehicles at the lower level allows modelers to capture transit network complexities such as transfers/missed connections, receiving a seat/standing and boarding/being rejected to board. This ‘hard’ implementation of vehicle capacity constraints at the lower level is aggregated into ‘soft constraints’ at the upper level for the least cost hyperpath calculation. Using a gap-based assignment procedure, user equilibrium is reached on large-scale networks in a computationally efficient manner. The algorithm is tested on the large-scale Chicago Transit Authority network. The gap-based approach outperforms the commonly used method of successive averages approach in terms of rate of convergence and quality of results. Furthermore, sensitivity analyses with respect to network parameters illustrate the robustness of the proposed two-level solution procedure

Stretching resources: sensitivity of optimal bus frequency allocation to stop-level demand elasticities

Bus transit route frequencies in practice are often set reactively, without consideration of ridership elasticity to the service frequency provided. Where elasticities are used in frequency allocation, a single across the board value or two respective values for peak and off-peak are used for the entire set of routes and stops throughout the day. With growing availability of ridership data, estimation of spatially and temporally disaggregated elasticities is possible. But do these make a difference in the resulting solution to the frequency allocation problem? This study is intended to examine this question by comparing the quality of solutions obtained using an optimal frequency allocation model with different sets of elasticities corresponding to varying levels of disaggregation. Three main methodologies for estimating ridership elasticity with respect to headway are compared in the context of a transit network frequency setting framework: (1) temporal elasticities based on time of day, (2) spatial elasticities via grouping stops into demand, supply and land use classes and (3) spatio-temporal elasticities using a linear regression model. Elasticities based only on temporal aggregation result in an underestimation of the potential improvements as compared to elasticities which account for some spatial characteristics, such as land use and the opportunity to transfer. It is also important to capture longer-term effects—over a year or more—because seasonal activity patterns may bias elasticity estimates over shorter time horizons