Bus Transit System Research Articles

Assessment of PM2.5 Concentration at University Transit Bus Stops Using Low-Cost Aerosol Monitors by Student Commuters.

Particulate matter of 2.5 µm and smaller (PM2.5) is known to cause many respiratory health problems, such as asthma and heart disease. A primary source of PM2.5 is emissions from cars, trucks, and buses. Emissions from university transit bus systems could create zones of high PM2.5 concentration at their bus stops. This work recruited seven university students who regularly utilized the transit system to use a low-cost personal aerosol monitor (AirBeam) each time they arrived at a campus bus stop. Each participant measured PM2.5 concentrations every time they were at a transit-served bus stop over four weeks. PM2.5 concentration data from the AirBeam were compared with an ADR-1500 high-cost monitor and EPA PM2.5 reference measurements. This methodology allowed for identifying higher-than-average concentration zones at the transit bus stops compared to average measurements for the county. By increasing access to microenvironmental data, this project can contribute to public health efforts of personal protection and prevention by allowing individuals to measure and understand their exposure to PM2.5 at the bus stop. This work can also aid commuters, especially those with pre-existing conditions who use public transportation, in making more informed health decisions and better protecting themselves against new or worsening respiratory conditions.

Enabling large-scale integration of electric bus fleets in harsh environments: Possibilities, potentials, and challenges

This paper presents a comprehensive and detailed investigation of electric bus transit systems, focusing on their feasibility in harsh environmental areas. The aspects that combine economics, operation, long-term performance, and reliability are all addressed and discussed in both short and long-term horizons. This work covers most of the “important to consider” criteria, compares the strategies applied in E-bus energy management, and recommends the best practices to follow in the presence of many system variables. Due to the significant impact of the ambient temperature, characterizing the battery performance under hot ambient temperatures is performed considering the existing Li-ion battery technologies. Moreover, the grid impact of large-scale charging systems is identified and classified by identifying the factors affecting the grid hosting capacity and permissible interaction levels. This work includes developing a comprehensive picture of the state of the art, emphasizing the gaps found in the literature, and proposing new venues for future research that help address the identified issues to enable reliable E-transportation plans.

The impacts of COVID-19 pandemic on bus transit demand: A 30-month Naturalistic Observation in Jiading, Shanghai, China

The COVID-19 pandemic and subsequent governmental responses have had unprecedented effects on public transit (PT) demand. This paper presents a naturalistic observation of the Jiading bus transit system in Shanghai, China, spanning from April 2021 to October 2023 and covering different stages under various extreme policy response combinations. We employ Prais-Winsten regression to quantitatively assess the pandemic’s impact on bus demand and explore demand recovery patterns at both aggregated and individual levels in the post-pandemic era. Our findings reveal a strong negative correlation between bus demand and the stringency of containment policies, consistent across both levels of analysis. In the post-pandemic period, ridership has only rebounded to 77% of the pre-Omicron near-normal level, with notable spatial and temporal disparities across different regions. While the temporal distribution of ridership has largely normalized, the recovery of travel demand between zones outpaces that of travel within zones. Moreover, a persistent decline in individual travel frequency has been observed, which has not reverted in the post-pandemic period. The insights from this study can help policymakers better respond to potential future crises and improve PT services in the post-pandemic era.

Co-optimizing the smart grid and electric public transit bus system

AbstractAs climate change provides impetus for investing in smart cities, with electrified public transit systems, we consider electric public transportation buses in an urban area, which play a role in the power system operations in addition to their typical function of serving public transit demand. Our model considers a social planner, such that the transit authority and the operator of the electricity system co-optimize the power system to minimize the total operational cost of the grid, while satisfying additional transportation constraints on buses. We provide deterministic and stochastic formulations to co-optimize the system. Each stochastic formulation provides a different set of recourse actions to manage the variable renewable energy uncertainty: ramping up/down of the conventional generators, or charging/discharging of the transit fleet. We demonstrate the capabilities of the model and the benefit obtained via a coordinated strategy. We compare the efficacies of these recourse actions to provide additional managerial insights. We analyze the effect of different pricing strategies on the co-optimization. Noting the stress growing electrified fleets with greater battery capacities will eventually impose on a power network, we provide theoretical insights on coupled investment strategies for expansion planning in order to reduce greenhouse gas (GH) emissions. Given the recent momentum towards building smarter cities and electrifying transit systems, our results provide policy directions towards a sustainable future. We test our models using modified MATPOWER case files and verify our results with different sized power networks. This study is motivated by a project with a large transit authority in California.

Development of passenger’s waiting time model at Bus Public Transit Terminal

Passenger’s waiting time at terminal is a key constituent of travel time, as long waiting time increases the overall journey time. In Nigeria, urban-rural bus transit system is associated with long waiting time. This study evaluates bus transit users’ waiting times at terminal with perceived and actual waiting times (PWT and AWT) along with associated bus service frequency (BSF). Bus public transit operating from Enugu city (as origin) to 6 local government areas (Awgu, Isi-Uzo, Nkanu west, Nsukka, Oji River and Udenu) of the state as destinations was studied. Data were collected on AWT and PWT at Old Park, Enugu for 30 days from 8 am to 6 pm (5 days per route). Data on AWT and PWT were collected based on passenger’s observation and oral interview, respectively. The study discovered that transit users incurred long waiting times, with 80 – 90% of the users incurring AWT of 21 minutes to 1 hour and more, indicating a poor service quality. Also, 80 – 90% of the users overestimated their PWT by 25 – 60%. At 95% confidence level, PWT is significantly longer than AWT (P < 0.05) for all the routes. A passenger’s perceived waiting time model was developed for predicting PWT based on AWT. The model developed showed that PWT is strongly correlated with AWT, with an R2 = 0.9591 and F-significance < 0.05. This suggests that that AWT accounts for 96% variability in PWT. Consequently, the model developed exhibits a reasonable accuracy, hence, can be applied for prediction of passenger’s perceived waiting time. The mean BSF for Enugu – Nsukka and Enugu – Oji River routes were 30 and 21 buses per day, respectively. While, the other 4 routes recorded lower values of 7 – 9 buses per day, resulting in longer waiting times than the other 2 routes. The implication of long passengers’ waiting time suggests the need for shifting from unscheduled to scheduled operation and improved BSF by shortening bus headways to minimize waiting time.

Evaluating Bus Transit System for Efficient Last Mile Transportation in Universiti Teknologi MARA (UiTM) Shah Alam

As the leading example in sustainable transportation, universities should have an optimal and precedent system. Universiti Teknologi MARA (UiTM) campus provides bus transportation for students. However, with the vast coverage of the campus area, the bus stops should be distributed evenly. This research aims to evaluate the bus stop position to find the optimal last mile distance concerning the principle of Universal Design, precisely the aspect of low physical effort. This research uses the method of space syntax to analyse the spatial arrangement of urban environments, including transportation systems. It is based on the idea that the spatial structure of an environment affects its usability and accessibility and that the arrangement of elements can be optimised to improve accessibility and reduce physical effort. As a result, this research finds that UiTM's current bus stop distribution needs to be improved. It should have filled the demand for several spots, such as dormitories, sports centers, and faculties. Adding bus stops or bus volume is the appropriate solution to this finding, considering the growth of student numbers and the upcoming LRT station development. This research has proven a new depth perspective by combining topologic, angular, and metriclogic. The findings of this research are beneficial in planning bus stops that carefully consider the universal design for all users wherever they live and work.

Vulnerability analysis of public transit systems from the perspective of the traffic situation

The critical to sustainable urban development that a robust bus transit system is constructed to alleviate urban congestion problems. Unfortunately, disruptions in bus transit systems can lead to dysfunction and threaten sustainable development. This paper proposed a vulnerability analysis model for constructing complementary urban bus networks (UBNs), which considers the actual capacity of the bus network at the speed of transport roads. In addition, the UBNs aimed analysis method is presented from three specific aspects: (a) utilizing the stochastic user equilibrium model and different traffic situations to measure the UBNs of passenger flow distribution according to multi-source data; (b) introducing the node capacity and structural, functional vulnerability analysis model by combining real traffic flows and the complex network theory; (c) analysing the vulnerability characteristics of UBNs cascading failures from different passenger flow distributions and increasing capacity parameters, and comparing with those under different attack methods. Then, the new vulnerability analysis model is applied to the Harbin trunk bus. Considering different attack scenarios, the UBNs show strong vulnerability in the case of high congestion. Compared with other deliberate attacks, the bus network based on the attack mode of the maximum intensity site is more vulnerable at different times. These insights could be used to inform a vulnerability-based spatiotemporal design of bus transit networks.

A resilient battery electric bus transit system configuration

Electric mobility is fundamental to combat climate change and attaining the United Nations Sustainable Development Goals (SDG-11). However, electric mobility necessitates a seamless integration between power and transportation systems, as the resiliency of both systems is becoming far more interdependent. Here, we focus on disruption to Battery Electric Bus (BEB) transit system charging infrastructure and offer a resilient BEB transit system planning model. The proposed model optimizes the BEB system costs while ensuring the system’s robustness against simultaneous charging station failures. In our case study, a single charging station failure would lead to up to 34.03% service reduction, and two simultaneous failures would reduce the service by up to 58.18%. Our proposed two-stage robust model addresses this issue with a relatively small added cost (3.26% and 8.12% higher than the base model). This cost enables uninterrupted BEB system operation during disruption, ensuring personal mobility, social interaction, and economic productivity.

A Dual-View Approach for Multistation Short-Term Passenger Flow Prediction in Bus Transit Systems

Timely and accurate prediction of bus passenger flow plays a crucial role in uncovering real-time traffic demand, presenting an essential and formidable challenge in the realm of bus scheduling and management. The extensive application of deep learning methods in transit passenger flow prediction can be attributed to their exceptional ability to effectively capture spatiotemporal features, resulting in superior performance. However, prevailing deep learning models in transit passenger flow prediction tend to ignore the data enhancement. Additionally, the predominant focus on a single station in the prediction task presents challenges in effectively capturing the spatiotemporal features of the entire network. A model named TSD-ST is proposed to better accomplish the task of predicting short-term transit passenger flow at multistation. The TSD-ST model leverages time series decomposition for data enhancement. Simultaneously, in addition to considering the adjacency graph, the similarity of all the stations of the entire transit network is also considered and uses multigraph convolution and graph fusion modules. This approach enables the TSD-ST model to effectively capture spatiotemporal dependencies. Experiments based on real-world bus transit datasets confirm that the TSD-ST model shows better performance in prediction tasks at 30-min, 60-min, and 90-min time scales, with an average improvement of 21.87%. The effectiveness of each component has been verified through ablation experiments.

Trip-based mobile sensor deployment for drive-by sensing with bus fleets

Drive-by sensing (i.e. vehicle-based mobile sensing) is an emerging data collection paradigm that leverages vehicle mobilities to scan a city at low costs. It represents a positive social externality of urban transport activities. Bus transit systems are widely considered in drive-by sensing due to extensive spatial coverage, reliable operations, and low maintenance costs. It is critical for the underlying monitoring scenario (e.g. air quality, traffic state, and road roughness) to assign a limited number of sensors to a bus fleet to ensure their optimal spatial–temporal distribution. In this paper we present a trip-based sensor deployment problem, which explicitly considers timetabled trips that must be executed by the fleet while a portion of them perform sensing tasks. To address the computational challenge in large-scale instances, we design a multi-stage solution framework that decouples the spatial–temporal structures of the sensing task through line pre-selection and bi-level optimization. As a result, the computational complexity is reduced to be sub-linear w.r.t. the number of lines, rather than combinatorial w.r.t. the number of buses in existing vehicle-based approaches. A real-world case study covering 400 km2 in central Chengdu demonstrates the effectiveness of the model in solving large-scale problems. It is found that coordinating bus scheduling and sensing tasks can substantially increase the spatial–temporal sensing coverage. We also provide a few model extensions and recommendation for practice regarding the application of this method.

Bus-Passenger-Flow Prediction Model Based on WPD, Attention Mechanism, and Bi-LSTM

The prediction of the bus passenger flow is crucial for efficient resource allocation, frequency setting, and route optimization in bus transit systems. However, it remains challenging for a single model to simultaneously capture the time-series data of the bus passenger flow with periodicity, correlation, and nonlinearity. Aiming at the complex volatility possessed by the time-series data of the bus passenger flow, a new hybrid-strategy bus-passenger-flow prediction model based on wavelet packet decomposition, an attention mechanism, and bidirectional long–short-term memory is proposed to improve the accuracy of bus-passenger-flow prediction. The differences between this study and the existing studies are as follows: Firstly, this model combines decomposition strategies and deep learning. Wavelet packet decomposition can decompose the original data into a series of smoother data components, allowing the model to be more adequate in capturing the temporal characteristics of passenger-flow data. And the model can consider the information after the predicted moment via backward computation. In addition, the model is equipped with the ability to focus on important features by incorporating an attention mechanism to minimize the interference of irrelevant information. Bus-passenger-flow prediction experiments are conducted using the Harbin bus-passenger-flow dataset as an example. The experimental results show that the model proposed in this paper can obtain more accurate bus-passenger-flow prediction results than the five baseline models can obtain.

A study on 5G performance and fast conditional handover for public transit systems

Fifth-generation (5G) networks are now in a stable phase in terms of commercial release. 5G design is flexible to support a diverse range of radio bands (i.e., low-, mid-, and high-band) and application requirements. Since its initial roll-out in 2019, extensive measurements studies have revealed key aspects of commercial 5G deployments (e.g., coverage, signal strength, throughput, latency, handover, and power consumption among others) for several scenarios (e.g., pedestrian and car mobility, mid-, and high-bands, etc.). In this paper, we make a twofold contribution. First, we carry out an in-depth measurement study of 5G in a large public bus transit system in a major European city. Second, based on the insights observed with the measurement study, we propose a new target cell selection criteria applicable to Fast Conditional Handover (FCHO), a 3GPP-specific 5G technique to foster reliable mobility. Our results are based on an extensive measurement campaign performed with several mobile phones connected to several mobile network operators totaling more than 1500 km over three months. The measurements reveal how flexible the network deployment is by analyzing Radio Resource Control (RRC) messages, mobility management and the suitability of our FCHO solution, and application performance.

Customer Satisfaction Analysis on the Integration of Bus Transit System in Semarang

Customer Satisfaction Analysis on the Integration of Bus Transit System in Semarang

Sustainable efficiency indicator for urban transportation: Case study of public bus and rapid transit systems in Bangkok

AbstractBangkok, the capital of Thailand, is confronting critical traffic congestion caused by the large number of private vehicles. In 2019, the city was ranked eleventh highest on the global traffic index. Public transport was proposed as the key to alleviating the problem. Nonetheless, the sustainability of public transportation should be evaluated from environmental, economic, and social perspectives to support policy decision‐makers and monitor the improvement. This research developed a sustainability indicator and used a case study to assess the sustainability of public transportation in Bangkok, by comparing the public bus to the rapid transit (electric rail transportation) service due to their high use rates in Bangkok. A sustainable efficiency indicator (SEI) was developed using the concept of eco‐efficiency and the negative impacts were extended to cover environmental, economic, and social aspects. The results showed that the environmental impact of the bus was greatest during the operational phase, whereas for rail transport, it was greatest during the construction phase. In terms of the economy, because the railway system had been designed to service a large population, an increase in passenger numbers for rail transportation could increase its sustainable efficiency. The social impact was assessed based on a satisfaction survey for each of the two types of transportation. All the impacts were converted into monetary values to calculate the SEI. The SEI could also be used to monitor the enhancement of sustainability effectiveness as a tracking performance indicator.

A dynamic network data envelopment analysis cross-efficiency evaluation on the benefits of bus transit services in 33 Chinese cities

ABSTRACT Our objective in this paper is to evaluate the benefits of bus transit. To obtain the more accurate and highly discrimination results, this study establishes an evaluation indicator system from a stakeholder perspective and proposes a novel combined method consisting of dynamic network data envelopment analysis, cross-efficiency evaluation, and Shannon entropy aggregation method. An empirical study of bus transit systems in 33 key Chinese cities from 2016 to 2019 is provided. The results show that the benefit of bus transit in most key cities was ineffective. In contrast to production efficiency, focusing on improving service effectiveness is more conducive to improving the benefits of bus transit. Small cities have higher benefits than large cities. Meanwhile, the benefits of bus transit vary geographically. This evaluation outcome provides a more appropriate basis for decision-making related to the benefits of public transit services, as well as for operational and management studies.

Balancing convenience and sustainability in public transport through dynamic transit bus networks

The rapid transformation of urban mobility fueled by the evolution of digital technologies has enabled smarter and more convenient modes of transportation. However, these new modes (e.g., ride-hailing and ridesharing) are not always more sustainable than traditional modes, such as public transport networks. In this work, we propose a dynamic transit bus system that aims at combining the higher convenience provided to passengers in on-demand systems with the sustainability of public transport. The goal is to attract more passengers to the transit service by offering reduced walk-to-station distance and total travel time compared to the fixed lines. Our proposed system plans the routes and intermediate stops of the transit lines based on the actual observed demand. Due to the complexity of the resulting optimization problem under investigation, we propose a solution method which splits the problem into four stages: clustering, initialization, optimization, and merging. We compare our proposed system in two settings: flexible (i.e., all intermediate stations are dynamic), and semi-flexible (i.e., original fixed lines intermediate stations are kept at the route and more dynamic stations are added) to the fixed static line. Our results show that our proposed system offers a more sustainable option and can reduce the system-wide travel distance by attracting more on-demand passengers to the transit service by offering them reduced walking distance and travel duration compared to the static lines. We also quantify the positive impact of passengers’ willingness to act more sustainably by accepting to walk more or arrive later to their destinations on the system-wide sustainability.

Multi-Objective Bus Timetable Coordination Considering Travel Time Uncertainty

This paper proposes a timetable coordination method for transfer problems in a bus transit system. With a given bus network, a stochastic mixed-integer linear programming (MILP) model has been formulated to obtain coordinated bus timetables with the objective of minimizing a weighted sum of the average value of total waiting time and its average absolute deviation value, allowing for random bus travel time. The vital decision variable is the terminal departure offset time of each target bus trip within a certain off-peak period. The robust MILP model can also be used to solve the first-bus transfer problem with the introduction of several new linear constraints. A solution method based on the Monte Carlo simulation has been developed to solve the MILP model. Numerical experiments have been conducted for different scenarios. The results indicate that bus timetables coordinated by the developed model are capable of substantially reducing waiting time for transfer and non-transfer passengers. In addition, the feasibility of simplifying a common sub-route into a single transfer stop in a timetable coordination problem has been explored based on numerical experiments.

A Formulated Approximated Solution for a Bus Transit System with Peak and Normal Periods

For a bus transit system with peak and normal periods, we derived an approximated formulated solution, to provide a patchwork for a published paper. Based on the same numerical example of the original model, our approximated solution attains the minimum cost within 0.3% to indicate that our approximated formulated solution is a good supplementary tool to estimate the optimal solution.

Social exclusion and the public bus system in Santiago, Chile

This research studies the relationship between transportation and social exclusion by observing the disadvantages faced by riders on the public bus transit system in Santiago, Chile. A qualitative stage consisting of four focus groups allowed us to identify different elements of the disadvantages that prevent the use of public buses. Afterward, a survey was constructed, obtaining a total of 512 valid responses. The sample selection criteria were stratified and non-probabilistic, following the exploratory research design, considering all districts of the city. We determined 8 dimensions upon factorial analysis: (1) safety, (2) service quality, (3) bus stops' infrastructure, (4) accessibility, (5) information, (6) drivers' behavior, (7) improvement in buses and (8) harassment. Together, these dimensions explained 58 % of the data variability. Although service quality and accessibility are the issues commonly highlighted in evaluations of the public transit bus service in Chile, this study shows that security is the dimension worst evaluated and presents a greater disadvantage. >50 % of riders had been unable to use public transit buses at least once, mainly due to safety concerns among women, young people, and the elderly. One relevant observation is the need to deepen transportation disadvantage research from a gender perspective. • 8 dimensions explained 58 % of the data variability on bus transport disadvantage. • Security is the dimension that presents a greater disadvantage. • Research on transportation disadvantage from a gender perspective is needed. • The elderly is affected by information disadvantage related to the digital divide. • Harassment reduces transport and social inclusion among women.

The accident-adjusted dynamic efficiency of public bus operators in metropolitan cities of India

PurposeThe study evaluates the accident-adjusted dynamic efficiency of public bus operators providing bus transportation services in eight major metropolitan cities of India.Design/methodology/approachThe slack-based measure (SBM)–undesirable window analysis approach is used to gauge the dynamic efficiency levels and identify the sources of inefficiency in bus transportation services. This innovative approach integrates the SBM model developed by Tone (2001, 2004) and the window analysis approach of Charnes et al. (1985). The main advantage of this approach is that one can explicitly incorporate the number of accidents in the production technology specification as an undesirable (bad) output and potently handle the issue of the “curse of dimensionality” in a small sample like ours.FindingsThe key empirical findings suggest wide variations in average efficiency levels across sample bus operators in metropolitan cities. The Chennai Transport Corporation is observed as the most efficient and consistent bus operator due to its most stable efficiency performance. The results additionally unveil that the role of managerial inefficiency was diminutive, and the scale-related issues were the real cause of sub-optimal or supra-optimal behaviour of sample bus operators in the resource-utilisation process.Practical implicationsThere is an urgent requirement for effective policy intercessions to mitigate the sizeable observed inefficiency in the production process and resolve scale-related issues of public bus operators offering transit services in major metropolitan cities of India.Originality/valueThis paper is maybe the first to assess the dynamic efficiency of public bus transit systems in India's major metropolitan cities after treating accidents.