Monash University, Institute of Transport Studies: World Transit Research (WTR)
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“How much should public transport services be expanded, and who should pay? Experimental evidence from Switzerland”
No full textThe twin challenge of increasing capacity to accommodate growing travel demand while simultaneously decarbonizing the transport sector places enormous pressure on public transport (PT) systems globally. Arguably the most fundamental policy choice and trade-off in designing and operating PT systems in the coming years will be service levels versus cost implications. On the presumption that public (citizen and consumer) opinion is crucial to making such choices, we study this question with a focus on Switzerland by using a factorial experiment (n = 1′634) that considers the frequency and geographic coverage of PT services as well as the cost implications for PT users and taxpayers. We find that support for increased frequency of connections and more services to peripheral regions is high as long as such service expansion is funded mainly by the government, rather than PT users. Preferences are generally consistent across subgroups, except in the case of government funding, where preferences differ by political orientation. This suggests that there is substantial demand across the board for PT services expansion funded primarily by the government, but that the question of funding is also potentially politically the most controversial. While our findings are specific to a country with a highly developed PT system, our research provides a template for similar research in other countries that struggle with a similar challenge
Bus dwell time estimation and overtaking maneuvers analysis: A stochastic process approach
No full textThe precise estimation of bus dwell time (BDT) is immensely important as it directly impacts the reliability of bus schedules and the accuracy of arrival time predictions. Various factors—such as the behavior of buses merging into the main traffic flow, uneven distribution of passengers on board, and frequent bus queues—jointly lead to a high variability of the BDT. To achieve an accurate estimation of BDT across various stop types, a comprehensive BDT model is developed in this paper. First, based on berth occupancy, probabilistic models are constructed for different bus arrival scenarios, which are then used to study corresponding bus queuing times. Second, the Bureau of Public Roads function is employed to characterize the obstruction effect occupants on the bus impose on boarding passengers. Combined with this function, a bus service time model is proposed considering the bus opening doors more than once and in-vehicle crowding. Third, the bus merging process is discretized to cope with the volatility of the time interval required for a bus to leave the bus bay. A Markov chain model is designed to calculate the time buses have to wait before merging. Case studies conducted in Qingdao, Chongqing and Beijing, China, demonstrate the effectiveness of the comprehensive model both during off-peak and peak periods. Furthermore, this paper incorporates overtaking rules into the comprehensive model to examine the influence of bus overtaking willingness and various overtaking policies on bus queuing times. Through numerical analysis, the key link is between willingness to overtake, service time and the likelihood of bus overtaking is disclosed
Electric bus charging scheduling problem considering charging infrastructure integrated with solar photovoltaic and energy storage systems
No full textBus fleet electrification is crucial in reducing urban mobility carbon emissions, but it increases charging demand on the power grid. This study focuses on a novel battery electric bus (BEB) charging scheduling problem involving solar photovoltaic (PV) and battery energy storage facilities. A mixed integer linear programming model is formulated to schedule BEB charging and control solar PV energy simultaneously. The model handles a range of realistic considerations, including heterogeneous BEBs regarding battery capacities, peak net charging power costs, flexible charging powers, and multi-route-multi-depot scheduling. A key point of our model is the introduction of variable charging power decisions designed to align BEB charging demands with solar PV production. The optimization objective is to minimize the sum of charging costs, carbon emission costs, energy storage costs, and revenue (negative cost) from solar PV energy sales. The model empowers public transport agencies to swiftly generate daily BEB charging schedules given daily solar and weather variations. A case study is performed in Beijing, China, utilizing actual bus trajectory data, weather conditions, solar irradiance, and detailed built environment data of bus depots. The results show that the proposed model can significantly reduce the operating cost and shift the charging loads by improving solar PV energy utilization
The impact of public transport priority policy on private car own and use: A study on the moderating effects of bus satisfaction
No full textChina started implementing a public transport priority policy in 2004 to encourage people to use public transit, especially buses, and reduce reliance on private cars. This paper used 334 questionnaire survey data from Changzhou City, China, and explored the moderating role of bus satisfaction based on the examination of the antecedents and consequences of affective motivation for private cars through two studies. Study 1 explored the antecedents of affective motivation for private cars and the moderating effect of bus satisfaction. The empirical results revealed that instrumental motivation is the most relevant predictor of affective motivation favoring private cars, followed by symbolic motivation. The moderating effect of bus satisfaction on the relationship between symbolic motivation and affective motivation was significantly negative. Study 2 explored the consequences of affective motivation for private cars and the moderating effect of bus satisfaction. Empirical results indicate that decisions regarding private car ownership and use are significantly negatively influenced by bus satisfaction. Bus satisfaction has a significantly positive moderating effect on the relationship between affective motivation and private car ownership and use. The marginal effect of bus satisfaction on the probability of simultaneously owning and using private cars is significantly negative, and it strengthens the influence of affective motivation in promoting both ownership and use of private cars. These results demonstrate that for individuals with experience using both buses and private cars, the public transport priority policy focused on improving bus service quality can effectively reduce private car ownership and use. These results can serve as evidence to support policymakers in continuing to enhance the public transport priority policy
ChatGPT for GTFS: benchmarking LLMs on GTFS semantics... and retrieval
No full textThe General Transit Feed Specification (GTFS) standard for publishing transit data is ubiquitous. With the advent of LLMs being used widely, this research explores the possibility of extracting transit information from GTFS through natural language instructions. To evaluate the capabilities and limitations of LLMs, we introduce two benchmarks, namely “GTFS Semantics” and “GTFS Retrieval” that test how well LLMs can “understand” GTFS standards and retrieve relevant transit information. We benchmark OpenAI’s GPT-3.5 Turbo and GPT-4 LLMs, which are backends for the ChatGPT interface. In particular, we use zero-shot, one-shot, chain of thought, and program synthesis techniques with prompt engineering. For our multiple questions, GPT-3.5 Turbo answers 59.7% correctly and GPT-4 answers 73.3% correctly, but they do worse when one of the multiple choice options is replaced by “None of these”. Furthermore, we evaluate how well the LLMs can extract information from a filtered GTFS feed containing four bus routes from the Chicago Transit Authority. Program synthesis techniques outperformed zero-shot approaches, achieving up to 93% (90%) accuracy for simple queries and 61% (41%) for complex ones using GPT-4 (GPT-3.5 Turbo)
Energy-efficient train control incorporating inherent reduced-power and hybrid braking characteristics of railway vehicles
No full textWith the increasing awareness of carbon neutrality, the application of energy-efficient train control (EETC) to rail transportation systems continues to attract attention from industry and academia. In many classic EETC studies, train models are commonly simplified with pure regenerative braking and constant power characteristics during high speeds, to simplify the complexity of the model. In this paper, a realistic model incorporating hybrid braking characteristics combining regenerative and mechanical braking, and reduced-power characteristics at high speeds into the EETC problem is proposed to improve control precision of the train and the modeling precision of the energy consumption and time. This study addresses the minimum-time train control (MTTC) problem and EETC problem considering nonlinear traction characteristics based on the mixed-integer linear programming (MILP) method, and nonlinear traction and braking characteristics are approximated via a piecewise linear (PWL) modeling technique. Results indicate that the proposed alternative models achieves some deviations from the realistic model in terms of time, energy consumption, and control strategies. The deviations between models in energy consumption and time accumulate as the number of operating stations increases. Therefore, the choice of an appropriate model depends on the precision requirements of various scenarios. In scenarios demanding higher precision, selecting the proposed realistic model is crucial for more accurate computation of energy consumption and time and for obtaining more precise control strategies
Urban rail-bus-walk network service integration towards accessibility and heat exposure consideration: Models and column generation solution approaches
No full textThe evolution of urban transportation systems is increasingly driven by the integration of rail, bus, and walking pathways, forming a cohesive service network. This urban rail-bus-walk service network integration is pivotal in addressing urban challenges and ensuring seamless mobility. Nevertheless, the effectiveness of such integrated systems depends on their accessibility and the willingness of individuals to take them as their primary transportation mode. In the context of climate change, extreme heat events pose an increasing threat to passenger comfort and health, which in turn, affects individuals’ decisions to use public transit. Additionally, walking to transit stops or waiting for transportation becomes more challenging during such extreme heat conditions. This paper tackles the challenge of comprehensively addressing the evolving issues related to public transit accessibility in response to rising temperatures, an area of research that has received limited attention until now. This paper introduced an approach aimed at optimizing the urban rail-bus-walk service network with a focus on both accessibility and heat exposure considerations. By calculating the transportation accessibility level under varying temperature conditions and identifying its vulnerability to high temperatures, this paper presents a methodology to enhance the transit service. Such improvement involves not only adjustments to the integration network but also considering of cooling facilities, such as shaded routes and shelters. Column generation is applied to solve this problem and the optimization result demonstrates improvement of accessibility under the extreme heat condition
Life cycle cost assessment of railways infrastructure asset under climate change impacts
No full textClimate change impacts such as extreme temperatures, snow and ice, flooding, and sea level rise posed significant threats to railway infrastructure networks. One of the important questions that infrastructure managers need to answer is, “How will maintenance costs be affected due to climate change in different climate change scenarios?” This paper proposes an approach to estimate the implication of climate change on the life cycle cost (LCC) of railways infrastructure assets. The proportional hazard model is employed to capture the dynamic effects of climate change on reliability parameters and LCC of railway assets. A use-case from a railway in North Sweden is analyzed to validate the proposed process using data collected over 18 years. The results have shown that precipitation, temperature, and humidity are significant weather factors in selected use-case. Furthermore, our analyses show that LCC under future climate scenarios will be about 11 % higher than LCC without climate impacts
Macroscopic modeling of mixed bi-modal urban networks: A hybrid model of accumulation- and trip-based principles
No full textNetwork-level traffic flow models either assume steady-state urban flows (i.e. accumulation-based models) or track the movement of all vehicles (i.e. trip-based models). The steady-state assumption present in the accumulation-based models may pose a challenge in light of the multi-modal nature of urban flows. It might be indeed a rough assumption for the flow of some transportation modes like buses, cruising-for-parking vehicles, taxis, and on-demand vehicles. Trip-based models address this concern, however, they need significant parameter calibration effort and are not computationally efficient, which substantially reduces the practicality of these models in real-world applications. Nevertheless, despite the critical importance of developing multi-modal traffic flow models, few attempts have been made to investigate these models in network macroscopic fundamental diagram (NMFD)-related literature. This paper bridges this gap by developing a hybrid network-level traffic flow model for mixed bi-modal (i.e. car and bus) networks. The present hybrid model reproduces the dynamics of car flows via accumulation-based model principles while tracking the movement of buses using the trip-based model. This effort also includes the development of a new FIFO-based entrance function to ensure different modes experience the same delay under saturated traffic conditions. Different numerical experiments are conducted to study the hybrid model performance and to compare it with that of accumulation-based and trip-based models in both steady-state and transition periods under different traffic conditions. Our observations reveal that the hybrid model simulates the dynamics of cars and buses by closely following the behavior of its components under free-flow conditions. The model also outperforms the accumulation-based model under saturated traffic conditions while being considerably less demanding than the trip-based model. A further investigation of the model performance is performed for networks with different bus shares in both free-flow and saturated traffic conditions, confirming the results of the initial numerical experiments. The hybrid model’s computational efficiency is demonstrated. The potential real-world applications of the hybrid model in development of bi-modal network-level simulation models, NMFD-based control strategies along with bus space allocation policies, public transport operation problems, modeling of cruising-for-parking vehicles, taxis, and on-demand vehicles, and modeling and application of autonomous modular vehicles are discussed and future research directions are highlighted
Real-time train regulation in the metro system with energy storage devices: An efficient decomposition algorithm with bound contraction
No full textFocusing on the energy-conservation train operation issues, this paper proposes an effective real-time train regulation scheme for metro systems with energy storage devices. Specifically, to minimize train timetable deviation, passenger waiting and energy consumption, we formulate a mixed-integer nonlinear programming model to generate energy-efficient train regulation strategies. This model explicitly considers the train traffic, passenger load and storage, immediate and delayed uses of regenerative energy. Carefully tailored to the proposed model, we devise an efficient decomposition algorithm to split the original problem into small-scale subproblems. In the algorithm, specific values of binary variables, passenger-flow estimates and logic-based cuts are consecutively identified and updated. Besides, bound contraction and bilinear-specific warming start procedures are particularly designed for further acceleration. Numerical experiments are conducted to validate the proposed model and method. Our energy-efficient train regulation strategies can improve train departure punctuality, headway regularity, reduce passenger waiting times, and achieve energy savings. Furthermore, the solution algorithm exhibits promising computational efficiency in real-world experiments, thereby facilitating an online implementation