Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Enhancing feeder bus service coverage with Multi-Agent Reinforcement Learning: A case study in Hong Kong
No full textPublic transport is a vital component of modern urban mobility, playing a significant role in reducing congestion and promoting environmental sustainability. Feeder bus services are essential for connecting residents to major public transport hubs, such as metro or rail stations. In this paper, a novel framework that enhances service coverage of the feeder bus while maintaining network efficiency is proposed. The framework integrates Multi-Agent Reinforcement Learning (MARL) to simulate and optimize route designs and frequency settings. Additionally, we introduce a Cost-based Competitive Coverage (CCC) Model to evaluate the performance of the feeder bus services by considering competition with other public transport modes. A case study conducted in two new towns in Hong Kong demonstrates the effectiveness and robustness of the proposed framework, highlighting its adaptability and potential to improve public transport accessibility
Joint charging scheduling of electric buses and active power flexibility integration
No full textPublic transport electrification stands out as a notable response to the environmental concerns in the transport sector. This study proposes a joint optimization framework for the coupled battery electric bus (BEB) transit system and active power distribution network (APDN) integrated with the flexibility of demand response (DR). The primary objective is to effectively support BEB mobility services by addressing their spatial and temporal charging demands. Special emphasis is placed on leveraging APDN capabilities to facilitate BEB operations with minimal costs. The problem is formulated as a bi-level stochastic programming by incorporating the non-profit agent at the upper level and the DR aggregators at the lower level. The upper level aims to minimize the joint costs of the APDN and BEB transit system, while the lower level seeks to maximize its profit through interaction with the upper level. The problem is then reformulated into an equivalent single-level model using Karush–Kuhn–Tucker conditions. The findings underscore the effective coupling framework in tackling the charging scheduling in the BEB sub-transit system in Skövde, Sweden, alongside the proper DR activation to meet the technical constraints of the coupled BEB transit and APDN. The proposed optimization framework can compensate for the additional burden of charging demands from BEBs by curtailing 6.4% of energy during peak hours
A low-cost automatic people-counting system at bus stops using Wi-Fi probe requests and deep learning
No full textCounting people is an important part of people-centric applications, and the increase in the number of IoT devices has allowed the collection of huge amounts of data to facilitate people counting. The present study seeks to provide a novel, low-cost, automatic people-counting system for the use at bus stops, featuring a sniffing device that can capture Wi-Fi probe requests, and overcoming the problem of Media Access Control (MAC) randomization using deep learning. To make manual data collection considerably easier, a “People Counter” app was designed to collect ground truth data in order to train the model with higher accuracy. A user-friendly, operating system-independent dashboard was created to display the most relevant metrics. A two-step methodological approach was followed comprising device choice and data collection; data analysis and algorithm development. For the data analysis, three different approaches were tested, and among these a deep-learning approach using Convolutional Recurrent Neural Network (CRNN) with Long Short-term Memory (LSTM) architecture produced the best results. The optimal deep learning model predicted the number of people at the stop with a mean absolute error of ~ 1.2 persons, which can be considered a good preliminary result, considering that the experiment was done in a very complex open environment. People-counting systems at bus stops can support better bus scheduling, improve the boarding and alighting time of passengers, and aid the planning of integrated multi-modal transport system networks
The effect of locating public transit stations on their walking accessibility using an actual street network
No full textTransit-oriented development is a need of the modern world. It not only has the potential to solve traffic-related problems but also needs to comply with sustainability and equity. As a case study, bus rapid transit of the twin cities Rawalpindi and Islamabad in Pakistan is examined. The study utilizes a GIS-based analysis that quantifies the impact of street network attributes surrounding transit stations on pedestrian accessibility to optimize the station location in an urban environment. A GIS application is used to model the actual pedestrian network around transit stations, where street network attributes like density, connectivity and configuration are evaluated. Walking accessibility indices are estimated based on street network attributes. Results revealed that there exists a trend in accessibility index values and street network attributes, where network configuration is the most significant aspect in explaining accessibility. The actual network-based analysis demonstrated in this study can assist planners and policy makers in the strategic positioning of future transit stations while selecting network attributes that are crucial for walking accessibility
Traffic signal priority control for public transport rapid transit based on a step-by-step prediction algorithm
No full textIn recent years, the problem of urban traffic congestion has become serious as the urban population increases rapidly around the world. In many countries, the widespread use of public transport (PT) in cities is a trend to solve the problems of urban environmental pollution and traffic congestion. The main point is to increase the efficiency of PTs. Although several methodologies have been proposed to reduce the travel time of PTs in urban road networks, there is still a gap in the research on efficient methods to reduce them effectively in relation to other vehicles. This paper proposes a traffic signal priority control method to reduce the travel time of PTs such as buses and trolleybuses based on a step-by-step prediction algorithm of the traffic flows at intersections. Based on the formularization of the queue lengths to the inflow and outflow of vehicles at intersections with four approaches, the next phase traffic signal control strategy is established using an algorithm which predicts the queue length and verifies its advantages. The formula of the queue length is updated by applying weight factors to the public transports and is applied to the prediction algorithm, of which efficiency is proved in various conditions. Simulation of Urban Mobility (SUMO), an open source traffic simulator, is used for verification. Simulation results show that the proposed step-by-step prediction algorithm remarkably reduces the waiting time of PTs while the weight factor is increased. Compared to the Longest Queue First Algorithm, the average waiting time of PTs is reduced by 30%, even when the influence on other vehicles is taken into account
Refining Cellular Data for Accurate Trip Chain Identification: A Novel Approach for Urban Travel Analysis
No full textCellular data play a crucial role in supporting travel demand assessment and urban traffic planning because of their cost-effectiveness and extensive coverage. However, inherent inaccuracies, such as imprecise positioning, data duplication, and abnormal communication frequencies, hinder their ability to depict travelers’ trips accurately. In this paper, we present a novel approach to mitigate these issues by employing base station mapping to refine positioning data, eliminating duplicates and abnormal frequency records. This refinement enables more effective utilization of cellular data for trip chain identification. We address the ping-pong handover effect by employing a finite automaton machine and an approximate nearest neighbor searching method with carefully selected seeds to identify activities. Our method’s accuracy is validated through ground truth value analysis, focusing on permanent resident population estimation and metro passenger flow estimation. Furthermore, we demonstrate the practical effectiveness and value of our proposed method through a series of representative applications in a real-world case study in the city of Nanning, Guangxi Province, China
Three-Stage Collaborative Optimization Method Under Bus Self-Coordinated Holding
No full textReducing the volatility in the operation of public transport is still an important consideration when seeking to maintain efficient and stable operations. In this study, stations and intersections are considered as a whole, and a self-coordinated holding strategy is developed based on the fluctuation of passenger flows and the characteristics of signals during off-peak periods. On this basis, a collaborative optimization is carried out in combination with signal priority and speed control. The mathematical model is established with the goal of minimizing passenger travel and enterprise operation costs. A section of the K2 line in Ganzhou City is selected for a case study. The improved discrete binary particle swarm optimization algorithm (DPSO) is then used for the calculations. The simulation and comparison of the arrival time distribution, headway deviation, and travel time index before and after the optimization show that reliability, stability, and efficiency have been improved by the optimization, which verifies the effectiveness of the optimization method
Why Did the Inflection Point of Bus Ridership Occur in China in 2014? Origins from the Effect of the Ride-Hailing Service
No full textAlthough rapid construction of public transit infrastructure has continued in China in recent years, there has been a significant decline in bus ridership since 2014. To investigate this trend, we conducted a statistical analysis for 24 cities in China, using fixed-effects panel regression to examine the relationships between bus ridership and significant factors from 2000 to 2019. Our analysis revealed that the emergence of ride-hailing services was probably the most significant contributor to the decline in bus ridership, reducing it by 33% from 2014 to 2019. This finding suggests that ride-hailing services have caused an inflection point in bus ridership. Furthermore, we found that gross domestic product (GDP) negatively moderates the effect of ride-hailing services on bus ridership, with cities of lower economic status experiencing a more significant decline in ridership, owing to the development of ride-hailing services. Our research provides valuable insights for policymakers and relevant departments when addressing transit ridership loss and transit system development issues
Urban Rail Transit Fare Reconciliation Method Using Multi-Source Data
No full textRevenue reconciliation is an important problem in allocating the fare revenues to different lines and operators in urban rail transit systems. This paper proposes a data-driven fusion method for fare reconciliation in public transport using mobile signal, smart card, and train operation data. It makes the best use of the complementary advantages of two of these data sources in inferring the passenger travel paths within the metro system (mobile signal data) and journey time distributions of origin–destination (OD) pairs (smart card data). We propose a nonlinear programming optimization model to adjust the inferred path fractions from mobile data by minimizing the theoretically derived and truly observed OD journey time distributions. Case studies using both synthetic data and real-world data to validate the model performance for the metro system in Nanjing, China. The results show that the proposed information fusion model can well approximate the true path fractions and the observed OD journey time distributions. The model is robust against the biased model inputs, such as the priori path journey time distribution, with a relative path fraction estimation error of 3% for the biased standard deviation level up to 30%. In addition, the model performs consistently better than the current fare reconciliation practices using mobile signal data in estimating OD path fractions
Regulating competition between transit and ride-hailing with transit priority zones
No full textThe thriving ride-hailing (RH) industry over the last decade provides passengers with flexible mobility options but also stimulates discussions about the possible cannibalization of public transport (PT) ridership. To foster PT and improve system efficiency, we propose a novel transit priority policy in which areas within a less-than-threshold distance to PT stops are announced as transit priority zones (TPZs). Passengers originating from TPZs must walk out of TPZs to hail rides unless exemption. However, RH services can still drop passengers within TPZs. Our model captures the interplay between passengers’ mode choices and both modes’ trip costs. We adopt an equilibrium-based approach to model passengers’ model choices in a stylized bi-modal system with a grid PT network. Passengers choose either PT or RH services, based on the mode-specified trip costs. Inversely, the trip costs of both modes are influenced by the modal split. Our model features a private RH agency that adjusts the price to maximize the net revenue. Under our settings, we prove theoretically that both the price and the total revenue of RH services are decreasing in the TPZ radius. We find numerically that TPZs help reduce the average cost for both PT and RH trips. However, the modal shift effect tends to be marginal when the RH agency adjusts the RH price to maximize its revenue. To further strengthen the policy’s impact, we consider a scenario where the RH agency offers first-mile and last-mile connection services within TPZs. This service enables passengers to use RH to reach PT stops, thus integrating RH and PT modes more effectively. Our numerical analysis indicates that providing such connection services not only enhances the impact of TPZs on the modal split but also preserves the effectiveness regarding reducing the system cost