Monash University, Institute of Transport Studies: World Transit Research (WTR)
Not a member yet
11112 research outputs found
Sort by
Identifying multi-modal deserts: A multivariate outlier detection approach
No full textProviding diverse modes of travel facilitates people\u27s access to jobs, healthcare, critical activities, and other services. To assess the equity of access to transportation services, it is essential to consider different travel modes. In this study, we propose a concept called “multi-modal deserts” and develop an approach to identify them. Multi-modal deserts refer to areas with limited mobility options, which restrict people\u27s access to essential services and opportunities. Based on the concept of multi-modality, our methodology integrates Mahalanobis distance for multivariate outlier detection to identify if an area\u27s mobility services significantly deviate from other areas considering road network factors and travel modes. Downtown Tampa, Florida, was selected as an empirical case to demonstrate the proposed method, and 11 multi-modal deserts were identified among 182 Census Block Groups. In addition, spider charts were used to illustrate and compare the characteristics of these multi-modal deserts. The results identified several multi-modal deserts with different poverty levels and transportation constraints. The insights can assist local authorities in identifying mobility gaps, allocating resources more effectively, and improving equal access to opportunities for all residents
How do subway stations encourage the vitality of urban consumption amenities in Shanghai: A perspective on agglomeration
No full textSubway is an effective public transportation infrastructure that attracts many urban consumer amenities in developing countries. This paper uses points of interest (POI) data from Dianping.com in 2020 in Shanghai to measure the quantity, quality, and diversity of consumer amenities by six indices: numbers, types, comments, ratings, star ratings, and takeout rate. We find that subway stations have a positive spatial correlation with vitality of consumer amenities within a 2-km radius. In addition, subway stations attract more newly added consumer amenities with higher quality within a 2-km radius, and the results remain robust by using the propensity score matching method. There exists heterogeneity in the ridership of subway stations. Subway stations with higher ridership have a greater effect on the consumer amenities and newly added consumer amenities. In terms of mechanism, based on the perspective of agglomeration economy, this paper uses Baidu Street View big data to verify that pedestrian flow is the key mechanism. This study accurately evaluates the economic and social benefits of subway stations and provides fundamental policy implications for the spatial layout of subways and consumer amenities of large cities in developing countries
Operating subsidies and transit efficiency: applying new metrics to old problems
No full textThis research revisits the perennial policy concern that operating subsidies hamper transit efficiency. We argue that the relationship between subsidies and efficiency can be better understood at the regional level and propose improved metrics related to transit efficiency. To begin, we focus on the impact of subsidies on transitsheds rather than transit operators to recast subsidy as a per resident metric, and we average vehicle load in the transitshed as our efficiency metric. Comparing these measures, we discover a surprising trend – transit efficiency is strongly and positively correlated with per resident operating subsidy. To explore this relationship further, we decompose per resident subsidy into federal and non-federal components and generate several new measures to improve modeling of transit efficiency at the transitshed level—subsidy revenue ratio, vehicle ratio, and guideway mile ratio (the latter two of which are scaled by “effective” population). We then apply a linear regression with these new measures on four years of data across the fifteen most populous transitsheds in the United States. Results suggest that operating subsidies promote transit efficiency (with federal subsidies being roughly three times as effective as non-federal subsidies) as long as the subsidies do not unduly outpace revenues. Results also suggest that the vehicle ratio is negatively associated with transit efficiency while the guideway mile ratio is positively associated. These findings offer support for operating subsidies that are reasonably offset by revenues and for targeting capital investments towards fixed guideway infrastructure rather than towards expanding fleet size
A stochastic programming model for designing bus bridging services under metro disruptions
No full textWith the growing reliance on urban metro networks, any accidental disruption can lead to rapid degradation and significant economic losses. Bus bridging services are common and efficient ways to minimize such adverse impacts. In this study, we investigate the problem of designing bus bridging services in response to unexpected metro disruptions, and propose a routing strategy with multiple bridging routes. In particular, to respond to uncertain factors such as passenger arrivals and bus travel times in the disruption environment, we develop a two-stage stochastic programming model for the collaborative optimization of bus bridging routes, schedules, and passenger assignments. To solve the computational challenges arising with the proposed model, a tailored tabu search algorithm is developed. Finally, several sets of numerical experiments are conducted and experimental results reveal that our proposed routing strategy can effectively improve the service level for the affected passengers during metro disruptions
Electric bus charging station location selection problem with slow and fast charging
No full textTo facilitate the shift from conventional to electric buses, the required charging infrastructure must be deployed. This study models the charging station location selection problem for fixed-line public transport services consisting of electric buses. The model considers the deadheading time of electric buses between the final stop of their trip and the locations of the potential charging stations with the objective of minimizing vehicle running costs. The problem is solved at a strategic level; therefore, several parameters of day-to-day operations, such as deadheading distances, are included as aggregate data considering their average values. In addition, it considers different charger types (slow and fast), which are subject to a day-ahead scheduling of the charging sessions of the buses. The developed model is a mixed-integer nonlinear program, which is reformulated as a mixed-integer linear program and can be solved efficiently for large networks with more than 1940 bus trips and 336 charging installation options. The model is applied in the Athens metropolitan area, demonstrating its potential as a decision support tool for selecting charging station locations and charger types in large public transport networks
A choice-based optimization approach for service operations in multimodal mobility systems
No full textMultimodal mobility systems provide seamless travel by integrating different types of transportation modes. Most existing studies model service operations and users’ travel choices independently or iteratively and constrained with pre-defined multimodal travel options. The paper proposes a choice-based optimization approach that optimizes service operations with explicitly embedded travelers’ choices described by the multinomial logit (MNL) model. It allows the flexible combination of travel modes and routes in multimodal mobility systems. We propose a computationally efficient linearization method for transformed MNL constraints with bounded errors to solve the choice-based optimization model. The model is validated using a mobility on demand and public transport network by comparing it with a simulation sampling-based MNL linearization method. The results show that the mixed-integer formulation provides a high-quality solution in terms of both the estimated choice probability errors and computational speed. We also conduct an error analysis and a sensitivity analysis to explore the behavior of the proposed approach. The real-world case study in Stockholm further illustrates that the analytical formulation achieves a better system operation performance than the traditional iterative supply–demand updating optimization method. The choice-based optimization model and solution formulation are highly adaptable for operations decision support integrating stochastic travel choices in multimodal mobility systems
Modeling ride-hailing and carsharing adoption & use patterns: deciphering the substitutive and complementary impacts of built environment, transit accessibility, & active travel
No full textThe rise of shared mobility services, including carsharing and ride-hailing, has transformative impacts on transportation systems. We present a behavioral framework to jointly model individuals’ carsharing and ride-hailing use with a focus on deciphering the substitutive vs. complementary roles of the built environment, transit accessibility, and active travel. Based on a sample of over 3,200 individuals from the 2019 Puget Sound Travel Survey, detailed travel behavior data are spatially integrated with neighborhood-level objectively assessed built environment and transit accessibility data. Joint heterogeneity-based multivariate ordered discrete choice models are specified to simultaneously account for random (unobserved) and systematic (observed) heterogeneity. The use patterns of carsharing and ride-hailing services exhibited positive dependence. Reflecting complementary impacts, neighborhood walkability, urban compactness, pedestrian-oriented urban design, and transit accessibility exhibited positive associations with individuals’ carsharing and ride-hailing use. Active travel behaviors (walking, biking, and transit use) also exhibited synergistic relationships with carsharing and ride-hailing use. While transit accessibility and active travel independently complement shared mobility services, our findings indicate that the interaction between the two could replace ride-hailing services. In particular, more physically active individuals (i.e., those engaging in greater active travel) may be choosing ride-hailing not out of preference but out of necessity due to lower transit accessibility around their home neighborhoods. Results suggest a mix of complementary vs. substitutive impacts, as opposed to the assumption of dichotomized (complementary or substitutive) impacts. Significant random and systematic heterogeneity in the behavioral, environmental, and demographic determinants of shared mobility services was revealed. We discuss the relevance and implications of the new findings considering scenario planning and travel demand modeling needs
Multiperiod line planning coordinately of urban rail transit by considering inter-period rolling stock connections
No full textTraditional rail transit line planning concentrates on a single period (e.g., an hour), which ignores plan synchronization and potentially results in unavailability of rolling stocks between periods. A multiperiod line planning (MLP) method is proposed to create a coordinated and efficient full-day line plan. To represent plan coordination between periods, a macro and special train connection network is constructed to describe the dynamic transfer process of inter-period rolling stock from one train route to another. A bi-level optimization model based on the connection network is developed. The upper level formulates MLP problem with rolling stock connections as a mixed integer linear model, and constructs a descent direction search by considering the optimality conditions of the lower-level problem. The lower level is a passenger flow assignment problem, solved using a label-setting algorithm. Finally, an empirical investigation of Beijing subway line 1 is conducted to validate the effectiveness of the proposed method. Results show that the multiperiod coordination plan effectively saves operation cost by 13.6% and passenger travel time by 3.45%, compared to the actual line plan. Besides, the impacts of rolling stock connections on multiperiod line plans become significant when system capacity is tight
Causal Graph Discovery for Urban Bus Operation Delays: A Case Study in Stockholm
No full textBus delays significantly affect urban public transportation by reducing operational efficiency and incurring high costs. Understanding the causes of these delays is essential for developing targeted mitigation strategies. While traditional research focuses on correlation-based analysis, it often fails to uncover the underlying causal mechanisms. This study examines various causal graph discovery algorithms combined with structural equation models (SEMs) to infer the causal relationships among factors that affect bus delays. These algorithms generate causal graphs for bus delays, revealing the interrelations and impacts of various operational factors. SEM is used to quantify the causal effects. This study evaluates the performance of these algorithms from the perspectives of both the statistical data fitting and the causal relationships generated. A case study is conducted using General Transit Feed Specification (GTFS) data from frequent bus routes in Stockholm, Sweden. The validation results demonstrate the effectiveness of data-driven causal discovery models in identifying causal links, particularly when combined with domain knowledge. The empirical analysis shows the complexity of factors contributing to bus delays, emphasizing the necessity of integrating causality into bus delay analysis. For example, a high correlation between origin delay and bus arrival delay (coefficient = 0.63) does not indicate direct causation, and a strong causation between dwell time and arrival delay does not imply a higher correlation (coefficient = 0.12). Comparing variable importance with linear regression (LR) reveals notable differences; origin delay, which is often overlooked by previous studies, is significant in the causal graph model (standardized coefficient = 0.601) but ranks much lower in LR (standardized coefficient = 0.003). These insights underscore the importance of automated, data-driven causal discovery in enhancing decision-making processes and improving the efficiency and reliability of transit services