Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Stressors for bus commuters and ways of improving bus journeys
No full textBased on the people-environment conceptual framework, this paper measures the level of transport stress experienced by bus commuters and examines its relationship with a wide range of environmental and personal stressors, as well as different stress responses. A total of 28 environmental stressors, 9 personal stressors, 4 time-related stressors and 6 stated stress responses are examined. The data were collected through a large-scale online questionnaire survey among bus commuters of a major franchised bus company in Hong Kong (n = 5,908). A factor analysis and a structural equation model (SEM) are conducted to unveil the interlinkages among bus-related environmental stressors, personal stressors, travel characteristics, perceived transport stress and stated stress responses. Results indicate that bus commuters reported an average stress level of 47.10 out of 100 (SD = 29.50). The average transport stress level is lower than money, work and family stress but higher than relationship stress. Spatially, there are great variations within the city, with notably higher stress levels for some new towns in Northwest New Territories and the central business district during evening commutes. Based on the SEM results, the number of bus transfers (β = 0.05) and travel time (β = 0.06) are strongly associated with transport stress. Poor bus stop environment (β = 0.14), crowding at bus stops and compartments (β = 0.13), hostile attitudes of passengers and drivers (β = 0.08), and unreliable waiting and travel time (β = 0.08) are important environmental stressors. Many of these factors have not been closely examined in previous studies. In relation to health, transport stress is positively associated with negative physical (β = 0.43) and emotional (β = 0.48) responses. Feeling exhausted, irritated and anxiety were the most commonly reported responses to transport stress. To enhance bus commutes, priorities should be given to improving the bus stop environment, reducing crowding throughout the bus journeys, and alleviating road congestion. Overall, the empirical findings from this large-scale study on transport stress can help governments to formulate targeted measures to enhance the quality of public transit services and to support sustainable urban transport
Assessing travelers’ preferences for online bus-hailing service across various travel distances: Insights from Chinese metropolitan areas
No full textThis study conducts a stated preference survey to assess travelers’ preferences for online bus-hailing service across various travel distances in Chinese metropolitan areas. By collecting personal attributes, travel characteristics, and scenario variables (including fares, walking distance, waiting time, and travel time for various ground transportation services), we develop a travel mode choice model based on XGBoost and examine the positive and negative effects and elasticity of different influencing factors. Empirical results demonstrate that the XGBoost-based travel mode choice model performs best in predictive accuracy, proving its superior fitting capability. Meanwhile, travel distance is pivotal in determining travelers’ preferences. Short-distance travelers prioritize time efficiency, whereas as travel distance increases, travelers become less sensitive to time, and fares become the primary consideration. This shift provides service operators with opportunities to implement differentiated strategies. The remaining travel modes also significantly influence choice preferences, with travelers critically concerned about the time cost of regular bus service and highly sensitive to taxi/car-hailing service fares. Personal attributes and travel characteristics, such as gender, age, and the number of companions, also impact choice preferences, providing opportunities for service operators to offer personalized services. Elasticity analysis further confirms the role of each influencing factor in increasing the attractiveness of choosing online bus-hailing service. Ultimately, we derive significant insights at the planning level, including single-factor optimization and prioritizing optimization for different dual-factor combinations. These insights serve as a basis for urban managers to formulate and enhance online bus-hailing service, covering aspects such as fare incentive policies and route service frequencies
Feeder bus service design under spatially heterogeneous demand
No full textIn rapidly sprawling urban areas and booming intercity express rail networks, efficiently designed feeder bus systems are more essential than ever to transport passengers to and from trunk-line rail terminals. When the feeder service region is sufficiently large, the spatial heterogeneity in demand distribution must be considered. This paper develops continuum approximation models for optimizing a heterogeneous fixed-route feeder network in a rectangular service region next to a rail terminal. Our work enhances previous studies by: (i) optimizing heterogeneous stop spacings along with line spacings and headways; (ii) accounting for passenger boarding and alighting numbers on bus dwell times and patron transfer delays at the rail terminal; and (iii) examining the advantages of asymmetric coordination between trunk and feeder schedules in both service directions. To tackle the increased modeling complexity, we introduce a semi-analytical method that combines analytically derived properties of the optimal solution with an iterative search algorithm. Local transit agencies can readily utilize this approach to design a real fixed-route feeder system.
This paper reveals many findings and insights not previously reported. For instance, integrating the heterogeneous stop spacing optimization further reduces the system cost (by 4% under specific operating conditions). The cost savings increase with demand heterogeneity but decrease with the demand rate and service region size. Choosing the layout of feeder lines where buses pick up and drop off passengers along the service region’s shorter side also significantly lowers the system cost (by 6% when the service region’s aspect ratio is 1 to 2). Furthermore, coordinating trunk and feeder schedules in both service directions yields an additional cost saving of up to 20%
Integrated Transit-Oriented Development (TOD) with suburban rail network design problem for maximizing profits
No full textDriven by the transit-oriented development (TOD) policy for rail stations, this study focuses on the network design of rapid suburban railways and the TOD zone design of corresponding suburban rail stations within metropolitan areas. A multi-level rail transit network was introduced, and the newly planned suburban rail network was considered as an expansion of the existing network to provide rapid services between urban and suburban areas. An integrated programming model was proposed to determine the physical and service routes of suburban rail network, the frequencies of each line, and the TOD plan along the selected rail stations. The integrated model is solved using the Adaptive Large Neighborhood Search (ALNS) algorithm. The effectiveness of the proposed model and methods is illustrated through numerical experiments, and several scenarios are designed for parameter analysis
Manage morning commute for household travels with parking space constraints
No full textParking has always been a thorny problem in urban transport systems. Parking availability and parking reservation management may have a significant influence on the travel behavior of household travelers. This paper explores the morning commute for household travels by considering parking space constraint and the parking reservation mechanism in a two-mode transport system. Household travelers depart from home to their children’s school by car or by rail and then go to work alone. For household travelers who drive, some household travelers have reserved parking spaces while other household travelers must compete for public ones on a first-come-first-served (FCFS) basis. When household travelers do not have a parking spot, they use rail transit for their commute. We derive ten equilibrium departure patterns based on the school-work time difference, the number of parking spots and the ratio of reserved parking spots. With these equilibrium departure patterns, we thoroughly discuss the impact of school-work time difference on the total travel cost of auto travelers and parking reservation ratio. It is found that, given the school-work time difference, appropriate parking reservation allocation can minimize the total system travel cost. Then, the system performance, including total system travel cost and total congestion cost, is examined by regulating the parking supply, the parking reservation allocation and the school-work time difference. We found that there is a unique optimal parking supply to minimize the total system travel cost, and appropriate staggering policy can alleviate traffic congestion and thus improves social welfare. Finally, numerical analysis is conducted to verify the findings, and the Pareto frontier for system performance is analyzed. Traffic authorities can select the optimal solution based on the preferences of traffic indicators to develop effective strategies to manage morning commuting of household travels with parking space constraint. The study demonstrates that implementing reasonable staggering policy and efficient parking management can effectively promote efficiency of urban morning commuting systems and reduce social costs for household travelers
Scheduling shared passenger and freight transport for an underground logistics system
No full textWith the introduction of freight transportation into the passenger transit network, underground city logistics are regarded as a desirable alternative to address the challenges due to truck movements in urban freight transport. Furthermore, some metro lines suffer from low-capacity utilization because of the unbalanced demand, which provides the potential to expand the extra capacity for freight transportation. Therefore, this study integrates the train unit scheduling problem with combined transportation of passengers and freight during off-peak hours. The objective is to fully utilize the remaining capacity by not only integrating passenger and freight flows but also allowing a flexible composition such that the capacity can better meet the demand. The composition of a trip is defined as its state. A three-dimensional space–time–state network is constructed to capture the composition transitions and passenger/freight trajectories. The problem is formulated as an integer linear programming model to minimize the weighted sum of train unit operational, passenger travel, and freight travel costs. Utilizing the problem-specific characteristics, a constrained-gap-based branch-and-bound approach is developed to efficiently solve the model. The worst bound for each objective is guaranteed by introducing two gaps in the passenger- and freight-related objectives. The nodes with estimated lower bounds exceeding the designated gaps are pruned. A beam search procedure is also included to further reduce the computational complexity. The developed algorithm is tested on real-life instances from the Beijing Metro Network. The results provide insights into the benefits and applicable scenarios for integrating passenger and freight transportation. Moreover, we demonstrate that the number of train units should be carefully determined considering the tradeoff between passenger service quality and freight demand volume
Adaptive rescheduling of rail transit services with short-turnings under disruptions via a multi-agent deep reinforcement learning approach
No full textThis paper presents a novel multi-agent deep reinforcement learning (MADRL) approach for real-time rescheduling of rail transit services with short-turnings during a complete track blockage on a double-track service corridor. The optimization problem is modeled as a Markov decision process with multiple control agents rescheduling train services on each directional line for system recovery. To ensure computational efficacy, we employ a multi-agent policy optimization solution framework in which each control agent employs a decentralized policy function for deriving local decisions and a centralized value function approximation (VFA) estimating global system state values. Both the policy functions and VFAs are represented by multi-layer artificial neural networks (ANNs). A multi-agent proximal policy optimization gradient algorithm is developed for training the policies and VFAs through iterative simulated system transitions. The proposed framework is implemented and tested with real-world scenarios with data collected from London Underground, UK. Computational results demonstrate the superiority of the developed framework in computational effectiveness compared with previous distributed control algorithms and conventional metaheuristic methods. We also provide managerial implications for train rescheduling during disruptions with different durations, locations, and passenger behaviors. Additional experiments show the scalability of the proposed MADRL framework in managing disruptions with uncertain durations with a generalized model. This study contributes to real-time rail transit management with innovative control and optimization techniques
Integrated timetabling and vehicle scheduling of an intermodal urban transit network: A distributionally robust optimization approach
No full textIntegrating emerging shared mobility with traditional fixed-line public transport is a promising solution to the mismatch between supply and demand in urban transportation systems. The advent of modular vehicles (MVs) provides opportunities for more flexible and seamless intermodal transit. The MVs, which have been implemented, are comprised of automated modular units (MUs), and can dynamically change the number of MUs comprising them at different times and stops. However, this innovative intermodal urban transit brings with it a new level of dynamism and uncertainty. In this paper, we study the problem of jointly optimizing the timetable and the vehicle schedule within an intermodal urban transit network utilizing MVs within the context of distributionally robust optimization (DRO), which allows MVs to dynamically (de)couple at each stop and permits flexible circulations of MUs across different transportation modes. We propose a DRO formulation to explore the trade-off between operators and passengers, with the objective of minimizing the worst-case expectation of the weighted sum of passengers’ and operating costs. Furthermore, to address the computational intractability of the proposed DRO model, we design a discrepancy-based ambiguity set to reformulate it into a mixed-integer linear programming model. In order to obtain high-quality solutionss of realistic instances, we develop a customized decomposition-based algorithm. Extensive numerical experiments demonstrate the effectiveness of the proposed approach. The computational results of real-world case studies based on the operational data of Beijing Bus Line illustrate that the proposed integrated timetabling and vehicle scheduling method reduces the expected value of passengers’ and operating costs by about 6% in comparison with the practical timetable and fixed-capacity vehicles typically used in the Beijing bus system
Flexible rolling stock composition strategy in urban rail transit lines: The influences of various train units and station capacities
No full textThis work studies the flexible rolling stock strategy in urban rail transit lines, by which various train units can be coupled to or uncoupled from the trains in certain stations according to the time-varying passenger demand. Due to the use of various train units, coupling and uncoupling train units must respect specific rules related to the shunting rules in adaptation stations, it is important to take into account the order of the train units in the trains. A mixed-integer linear programming model is proposed to optimize the rolling stock compositions together with the train timetable. Additionally, the model includes constraints for passenger assignment to evaluate the number of passengers in train services or stranded at stations. The model aims at reducing the number of stranded passengers, the number of rolling stocks, and the operational costs. To efficiently solve the integrated optimization problem, this study proposes an approximation approach and a two-stage heuristic algorithm. The approximation approach relaxes some calculations and constraints in an approximate manner in order to improve the computational speed. The two-stage heuristic approach divides the integrated optimization problem into two subproblems and solves the two subproblems with a heuristic searching method. The proposed optimization method is examined using the historical operation data of Shanghai Metro Line 16. The results suggest that the proposed approach can well satisfy the passenger travel demand and decrease operational costs. In addition, the influences of train unit types, headway control strategy, and station capacities are studied. The findings of the case studies can provide insights into timetable and rolling stock schedule planners
Optimizing block configuration and operation protocol for extra-long metro trains
No full textIn many densely populated mega-cities around the world, metro systems are becoming overly saturated by the ever-increasing travel demand, which results in overcrowded stations, excessive delay, and unsafe anxieties among riders. The concept of extra-long trains (i.e., trains longer than station platforms) was recently proposed in the literature as a promising way to increase a metro line’s capacity without additional infrastructure construction. This paper develops a general modeling framework to optimize train block configuration and operation protocols of a metro line so that extra-long trains can be used under varying demand distribution and infrastructure setting. The design problem is formulated as the integration of two coupled vehicle routing problems which simultaneously optimizes the train block configuration plan, door opening strategy, stop skipping strategy, and train dispatch schedule. This paper proposes two customized solution methods, including an adapted savings heuristic and a destroy-and-repair algorithm. A series of hypothetical examples are tested to demonstrate how the proposed solution approaches outperform an existing commercial solver even for small to moderate problem instances. Moreover, two real-world case studies, with very different demand patterns from two continents, are presented to test the effectiveness of using extra-long trains. Our results show that a metro line with extra-long train operations is capable of serving up to 20%–30% more passenger demand as compared to that with only regular trains, while at the same time producing a similar or lower passenger average travel time