Bus Stop Research Articles

Understanding the interplay among urban public transport modes: Spatial variation and built environment effects

Research on public transport systems is continually driven by the societal benefits of transport services. However, the nuanced effects of the built environment on public transport mode shares across different modes have received little attention. Using multi-source travel data from Shenzhen, this study presents an analytical framework, which integrates the light gradient boosting machine and Shapley additive explanations, to delve into the complex interplay among public transport modes. The results reveal that most origin-destination pairs with available metro services exhibit a metro share exceeding 50%, underscoring the high attractiveness of the metro. Travel distance emerges as the primary determinant of mode share, with transport-related characteristics proving more influential than land use modifications. This study identifies nonlinear effects of the built environment on mode share, with specific thresholds for built environment characteristics suggesting fine-tuned planning strategies. Metro station densities of 2 counts/km2 correlates with increased metro share, while bus stop densities of 15 counts/km2 are associated with higher bus share. Areas within 2 km of a metro station or with bus stop densities below 15 counts/km2 tend to have higher bike shares. Specific land use ratios and high levels of land use mix are linked to increased shares of certain modes. The findings suggest optimizing bus services in relation to metro availability and offer guidance for balanced planning in promoting metro and shared mobility to achieve a sustainable transport system.

Multi-lane cellular automata model considering bus-HOV lane

To quantify the impacts of high occupancy vehicles (HOV) entering the exclusive bus lane on road traffic efficiency, the car following and lane changing characteristics of bus, HOV and non-HOV were analyzed when setting the bus-HOV lane. Using the theory of cellular automata (CA) and combining the control rules of the bus-HOV lane, the lane occupied by a vehicle and the type of a vehicle regarded as constraints were introduced into the asymmetric lane changing rules, and then a multi-lane CA model was established for the car following and lane changing of multi-type vehicles considering the bus-HOV lane. MATLAB software was used to accomplish numerical simulation to analyze the specific effects of the proportion of HOV, bus departure frequency and bus stop spacing on the traffic flow parameters and their relationships. The results show that: when the proportion of HOV is less than 0.5 on a three-lane road segment, setting the bus-HOV lane can not only improve road traffic efficiency but also ensure the speed of bus operation under the condition of high initial space occupancy; when the proportion of HOV exceeds 0.5 , it is difficult to improve road traffic efficiency by restricting non-HOV from entering the bus-HOV lane; when the initial space occupancy is greater than 0.2 , the higher the bus departure frequency is, the greater the decline of traffic volume of the bus-HOV lane is; when there are 5 bus routes to dispatch a bus every 3 minutes, compared with the low initial space occupancy, the traffic volume of the bus-HOV lane under the condition of the high initial space occupancy reduces by about 30%; the bus stop spacing more significantly affects the traffic efficiency of the bus-HOV lane, the longer the bus stop spacing is, the higher the frequency of HOV lane changing is, the more the increase of the congestion segments on the bus-HOV lane and the adjacent normal lane is, thus the traffic efficiency of the whole road is influenced.

An Enhanced Transportation System for People of Determination.

Visually Impaired Persons (VIPs) have difficulty in recognizing vehicles used for navigation. Additionally, they may not be able to identify the bus to their desired destination. However, the bus bay in which the designated bus stops has not been analyzed in the existing literature. Thus, a guidance system for VIPs that identifies the correct bus for transportation is presented in this paper. Initially, speech data indicating the VIP's destination are pre-processed and converted to text. Next, utilizing the Arctan Gradient-activated Recurrent Neural Network (ArcGRNN) model, the number of bays at the location is detected with the help of a Global Positioning System (GPS), input text, and bay location details. Then, the optimal bay is chosen from the detected bays by utilizing the Experienced Perturbed Bacteria Foraging Triangular Optimization Algorithm (EPBFTOA), and an image of the selected bay is captured and pre-processed. Next, the bus is identified utilizing a You Only Look Once (YOLO) series model. Utilizing the Sub-pixel Shuffling Convoluted Encoder-ArcGRNN Decoder (SSCEAD) framework, the text is detected and segmented for the buses identified in the image. From the segmented output, the text is extracted, based on the destination and route of the bus. Finally, regarding the similarity value with respect to the VIP's destination, a decision is made utilizing the Multi-characteristic Non-linear S-Curve-Fuzzy Rule (MNC-FR). This decision informs the bus conductor about the VIP, such that the bus can be stopped appropriately to pick them up. During testing, the proposed system selected the optimal bay in 247,891 ms, which led to deciding the bus stop for the VIP with a fuzzification time of 34,197 ms. Thus, the proposed model exhibits superior performance over those utilized in prevailing works.

Air Quality Improvement at Urban Bus Stops: Optimal Air Purification Placement Using CFD

Nitrogen dioxide (NO2) levels are often elevated near roadways due to vehicle emissions, while sulfur dioxide (SO2) is predominantly found near petrochemical complexes as a result of industrial activities such as oil refining and chemical manufacturing. Considering the detrimental effects of these emissions on the environment and human health, the optimal placement of air purification systems at two bus stops in Ulsan, a heavily industrialized city in South Korea, was investigated in this study to reduce NO2 and SO2 concentrations. Computational fluid dynamics (CFD) simulations were performed to identify strategic installation locations, resulting in a significant reduction in pollutant levels. The largest impact was noted for the Deokha Market bus stop, whereby the added health risk (AR) decreased by 1.93% and the exposure reduction effectiveness (ERE), a measure of air purification system efficiency, increased by 13.8%. Similarly, at the Hyomun Intersection bus stop, placing the system near the sidewalk led to a significant reduction in AR by 1.60% and an increase in ERE by 11.63%. Additionally, air purification systems at Ulsan bus stops are expected to reduce NO2 levels by 9.1 ppb, decreasing mortality risk by 1.44%, saving 7 lives annually, and yielding an economic benefit of 33.06 million USD.

Influence factors on travel mode preference of working residents living far away from downtown area on workdays: A hybrid method integrating random parameter logit model and Apriori algorithm

This study presents a detailed analysis on the characteristics of travel mode preference of working residents living far away from downtown area on workdays, using GPS-based activity travel diary data from Shangdi area (Beijing). A hybrid method integrating random parameter logit model with systematic heterogeneity (RPL-SH) and Apriori algorithm is put forward to explore the influence factors and interaction effects affecting travel mode preference. First, the RPL-SH model is established to explore significant factors, and capture the unobserved random heterogeneity and systematic heterogeneity due to individual characteristics on the travel mode preference. Then, these significant factors are used to generate association rules by Apriori algorithm to investigate statistical associations between the specific travel mode preference and these significant factors. Ten significant factors are found in the RPL-SH model, in which annual household income is normally distributed. The results of the Apriori algorithm indicate that some factors combined with other factors could significantly influence working residents’ travel mode preference. For example, the combination of lower annual household income and shorter distance between workplace and the nearest bus stop is highly associated with green travel mode preference. Moreover, the results show that the proposed hybrid method not only demonstrates the consistency of the results of the two methods, but also plays a complementary role in exploring more information on travel mode preference. This research hopes to give regulators a better understanding on how working residents living far away from downtown area choose their travel mode, so as to develop more effective and targeted measures for reducing private car use and alleviating workday traffic congestion.

An approach for two-dimensional convolutional neural networks for hourly passenger boarding demand prediction based on uneven smart-card data

An invaluable resource for understanding passenger boarding patterns and forecasting future travel demand is the tap-on smart-card data. Positive instances, on the other hand—boarding at a given bus stop at a certain time—are less common than negative instances when looking at the smart-card data (or instances) by boarding stops and by time of day. Machine learning algorithms that are used to estimate hourly boarding numbers at a certain location have been shown to be much less accurate when the data is imbalanced. Before using the smart-card data to forecast bus boarding demand, this research tackles the problem of data imbalance in the data. To create fake traveling instances to add into a synthetic training dataset containing more evenly distributed traveling and non-traveling examples, we suggest using deep generative adversarial networks (Deep-GAN). Next, a deep neural network, or DNN, is trained on the synthetic dataset to predict which instances from a given stop in a certain time frame will travel and which ones won't. According to the findings, resolving the data imbalance problem may greatly enhance the predictive model's functionality and make it more accurate in predicting ridership profiles. The suggested strategy may create a synthetic training set with a better similarity so diversity and, therefore, a stronger prediction capability, according to a comparison of the Deep-GAN's performance with other conventional resampling techniques. The study emphasizes the importance of the issue and offers helpful recommendations for enhancing the quality of the data and model performance for individual travel behavior analysis and travel behavior prediction.

Bus Bunching at Bus Stops and Its Spatiotemporal Patterns along Urban Bus Routes

Bus Bunching at Bus Stops and Its Spatiotemporal Patterns along Urban Bus Routes

Optimization of Electric Bus Scheduling Using Genetic Algorithm: A Case Study in Public Transport of UNNES Campus Area

The transportation service system requires improvements to evolve into a smart and more efficient system. Passengers waiting at bus stops can create long queues, causing a lack of available shuttle bus capacity when arriving at the bus stop. This work proposes a genetic algorithm model to minimize passenger waiting time and schedule shuttle buses to stops with high capacity. The Genetic Algorithm works by searching for the optimal value to result in optimal waiting time by providing calling shuttle bus. After the method reaches the optimal solution, the simulation result will provide a minimum waiting time. In case studies of simulated design at either campus in Central Java, Indonesia. This method provides a simulated system shuttle bus on scheduling to raise a challenge in waiting time efficiency and passenger accumulation at campus transportation. The case studies of the application on passenger waiting time showcase the model's ability to improve transportation services in the unscheduled campus area. This system was designed to ensure that it was effective in addressing the transportation challenges faced by students and staff. Use the full potential of bus transportation in the campus area to ensure continuity between stops and city transportation. Therefore, this approach reduces waiting times and schedules to overcome challenges posed by passenger accumulation for structured campus transportation services. Keywords—Shuttle Bus; Genetic Algorithm; Campus Area; Minimize Waiting Time; Scheduling; Optimization.

Modelling connected and autonomous bus on dynamics of mixed traffic in partially connected and automated traffic environment

Since connected and autonomous buses (CABs) have great potential of being operated in partially connected environment and raise great impact on mixed traffic flow, this study develops an analytical model for modeling system dynamics of mixing connected and autonomous vehicles (CAV), human-driven vehicles (HV), and CABs in both cases of dedicated bus lane and non-dedicated bus lane, in which the impact of CABs is specifically quantified under varied levels of connected vehicle penetration. A Vissim-Matlab simulation evaluation is also developed for validating the model’s effectiveness. The results demonstrate that the proposed model performs well with an accuracy of over 85%, and it can be up to 95% in case of non-dedicated bus lane when the inflow rate is higher than 2000veh/h. Besides, the impacts of CABs’ volume, bus dwelling time, and bus stop location on the performance of mixed traffic flow are verified in two CAB operating environments. The obtained results can be used as the basis for planning and operating CABs in partially connected environment.

Evaluation of Public Transportation System through Social Network Analysis Approach

In response to the phenomenon of global warming, the transportation sector aims to mitigate carbon emissions by promoting the use of public transportation. This study employs social network analysis to propose effective improvements to the public transportation system, focusing on bus stop locations and route networks in Hwaseong City, South Korea. Two networks were constructed based on existing public transportation routes and usage data at each bus stop. The findings and implications are as follows: Analyzing the public transportation network from a network perspective can effectively contribute to improving the public transportation network route system. By evaluating centrality and brokerage for the existing routes, it is possible to identify inefficient routes and develop efficient route modification plans. Based on actual usage patterns, excessive bus supply and unnecessary bus stop locations can be identified, allowing for the establishment of appropriate operational plans. This can lead to improved operational efficiency and cost savings. Rational route design and operational planning can enhance public transportation services and promote increased use of public transportation. Ultimately, this contributes to sustainable development through carbon reduction in the transportation sector.

A travel time matrix data set for the Helsinki region 2023 that is sensitive to time, mode and interpersonal differences, and uses open data and novel open-source software

Travel times between different locations form the basis for most contemporary measures of spatial accessibility. Travel times allow to estimate the potential for interaction between people and places, and is therefore a vital measure for understanding the functioning, sustainability, and equity of cities. Here, we provide an open travel time matrix dataset that describes travel times between the centroids of all cells in a grid (N = 13,132) covering the metropolitan area of Helsinki, Finland. The travel times recorded in the dataset follow a door-to-door approach that provides comparable travel times for walking, cycling, public transport and car journeys, including all legs of each trip by each mode, such as the walk to a bus stop, or the search for a parking spot. We used the r5py Python package, that we developed specifically for this computation. The data are sensitive to diurnal variations and to variations between people (e.g. slow and fast walking speed). We validated the data against the Google Directions API and present use cases from a planning practice. The five key principles that guided the data set design and production – comparability, simplicity, reproducibility, transferability, and sensitivity to temporal and interpersonal variations – ensure that urban and transport planners, business and researchers alike can use the data in a wide range of applications.

Bus stop design strategy based on visual comfort level in Bandung City

High sunlight and poor air quality in the city of Bandung affect the level of visual comfort. In designing bus stops, space is needed that has visual comfort so that the facilities are maintained and can function well. This research was conducted to determine visual comfort as measured using the Daylight Factor (DF) at bus stops in Bandung. The method used is a quantitative experiment using Drajmarsh and Ladybug software. To identify a bus stop design with stable visual comfort, analysis was conducted at various times and places. The results of the study are guidelines for creating publicly accessible spaces, such as bus stops, in Bandung that make utilization of natural light from the sun from morning to night. This light is crucial for visual information, thermal comfort, and building energy efficiency.

Capacity Reduction for Urban Road Due to Curbside Bus Stop: A Case Study of Srijana Chowk, Nepal

Curbside bus stops have a significant impact on traffic characteristics, including speed and roadway capacity. Srijana Chowk, the urban area within Pokhara metropolitan city, was selected for the study. The objective of the study is to evaluate the reduction in roadway capacity at the curbside bus stop at Srijana Chowk. The primary data was collected using videographic techniques. The analysis indicated that Srijana Chowk’s curbside bus stop caused an average stream speed reduction of 6.23%. The capacity at the Srijana Chowk bus stop was 2532 PCU/hr and 2718 PCU/hr at the curbside and on the opposite side of the bus stop, respectively. Similarly, the capacity reduction was analyzed to be 16.62% and 6.84% by the Indonesian Highway Capacity Manual and Greenshield’s model, respectively

Heat wave resilience in open Spaces: A case study of a Self-Sufficient cooling shelter

Climate change intensifies urban uninhabitability due to increasingly frequent heat waves. To alleviate it, climatic shelters emerge as an initiative for the research community, which should deepen the integration of technologies that use natural resources for cooling. This work aims to evaluate a novel climatic shelter by characterizing its thermal behavior through an experimental study at a bus stop. It has the capacity to alleviate the thermal stress of its occupants thanks to cooling modules. The results allow to determine the capacity of the natural water-cooling system and the level of thermal storage, allowing to quantify the impact on the citizen by means of the COMFA comfort index. It is corroborated that the chilled water temperature reduces the surface temperature by 15 °C. The installation reduces overheating caused by solar radiation by 75 % compared to traditional shelters. It also demonstrates energy and electrical self-sufficiency even during high intensity heat waves. The shelter maintains daytime water temperature between 18 and 25 °C, even in heat waves exceeding 7 days and 45 °C air temperature. The COMFA index has been reduced to values between 70 and 120 W/m2 in 90 % of the worst weather conditions in Csa climates by Köppen Geiger, for the months of July and August 2023.

Efforts to reduce illegal parking in Lampung Province

Purpose: The formulation of the problem in this study is how land is prohibited for parking and the efforts to suppress illegal parking in Lampung Province. Research Methodology: The method used in this study was descriptive and qualitative. Results: The results of this study are There are 10 no-parking areas that must be obeyed, including: Bends, shoulders or a bridge, On pedestrian or bicycle tracks, Near traffic lights, On main roads, Facing or close to other stopped vehicles, Within 6 meters (20 feet) of an intersection, or within 9 meters (30 feet) of a bus stop, unless it is damaged. Then, they do not stop or park 3 m (10 feet) on the other side of a fire hydrant, or which may interfere with the access of firefighting vehicles to the hydrant, along slippery roads, on overpasses, tunnels, or on the side of the road leading to the overpass or tunnel, on grass verge or road shoulder. The efforts of the Lampung Province Transportation Agency to deal with illegal parking constraints are as follows: The Department of Transportation must perform mapping, good parking management not only helps in traffic management but also increases local revenue (PAD), the importance of awareness from the community and the government, the government must be present and provide guidance and ensure that parking is managed properly, there are sanctions for violators of indiscriminate parking, but the emphasis is more on education and guidance than sanctions alone.

A seamless bus network without external transfers using autonomous modular vehicles

Recently, autonomous modular vehicles (AMVs) capable of enabling in-motion transfer (IMT), i.e. passengers moving between vehicles while the vehicles are traveling, have been developed. IMT allows new paradigms for public transport operations that can enhance the speed, flexibility, and reliability of the system. We leverage IMT technology to propose the first network-wide seamless, stop-less bus service concept. We recently introduced the concept of a Stop-Less Autonomous Modular (SLAM) bus service, which eliminates the need for passengers to stop at each bus stop while traveling along a bus line. The current work extends this paradigm to the network level by developing a bus service framework in which multiple SLAM bus lines operating on main arterials interact by exchanging pods among each other to provide passengers with a travel experience that not only eliminates most extra passenger stops within a single line, but also eliminates external transfers, i.e. it removes the need to disembark at a bus stop to change buses when transferring from one bus line to another. The result is a high-speed service that provides a completely seamless journey from the passenger’s origin stop to destination stop anywhere in the coverage area, with integrated (i.e. internal) transfers that take place inside the vehicles themselves while they are in-motion. Our results show that while the proposed service has more stringent operating requirements than a comparable conventional bus service, it provides significantly superior performance, reducing passengers’ travel cost by up to 35% under realistic system settings.

Customized Bus Stop Location Model Based on Dual Population Adaptive Immune Algorithm

Selecting optimal locations for customized bus stops is crucial for enhancing the adoption rate of customized bus services, reducing operational costs, and, consequently, mitigating traffic congestion. This study leverages ride-hailing data to analyze the distance between passengers and bus stops, as well as the operational costs associated with establishing these stops, to construct a customized bus stop location model. To address the limited local search capability of conventional immune algorithms, we propose a Dual Population Adaptive Immunity Algorithm (DPAIA) to solve the bus stop location problem. Finally, we conduct simulation experiments using passenger travel data from a ride-hailing company in Chengdu to evaluate the proposed customized bus stop location model. Through simulations with Chengdu ride-hailing data, the DPAIA algorithm minimized the weighted cost to CNY 28.95 ten thousand, outperforming all counterparts. Although proposing 9–11 more stops than competitors, this increase slightly impacts costs while markedly reducing passenger walking distances. Optimizing station placement to meet demand and road networks, our model endorses 50 strategic bus stops, enhancing service accessibility and potentially easing urban congestion while boosting operator profits.

Optimizing quasi-dynamic wireless charging for urban electric buses: A two-scenario mathematical framework with grid and PV-battery systems

This paper presents a mathematical optimization framework for the strategic placement of quasi-dynamic wireless charging (QWC) stations within road networks to address the charging needs of battery electric buses (BEBs). This study evaluates two scenarios for powering the buses. In the first scenario, a grid-connected system is considered. The optimization aims to minimize annual costs related to capital, operation, and energy losses of the electric bus fleet. This involves determining the optimal locations for QWC stations, the length of power transmitters, and the corresponding battery capacities for the BEBs. Using MATLAB-based optimization tools Casadi and Yalmip, with solvers Bonmin and Fmincon, the optimal configuration includes a 13 kWh battery capacity and a 300 m power transmitter distributed across five bus stop areas. The second scenario employs a chance-constrained optimization approach for an isolated solar photovoltaic (PV) and battery energy storage system (BESS). This system is designed to reliably meet the BEBs' energy requirements throughout the day, considering different seasonal data (winter, summer, all seasons/year-round). The optimization results for the PV and BESS capacities vary with the seasons: 394.247 kW and 2012.6 kWh using summer data, 1762.1 kW and 2738.2 kWh using winter data, and 1610.8 kW and 2741.9 kWh using year-round data. Additionally, the paper examines the impact of varying bus fleet sizes on the optimal battery size and power transmitter combination using a real-world example of the bus route between Khalifa City and Abu Dhabi Downtown in the UAE. The findings suggest that larger batteries with fewer or no charging stations are more economical for smaller fleets. Conversely, as the fleet size increases, a combination of smaller battery sizes and a greater number (and length) of QWC (power transmitters) becomes more cost-effective. This research offers significant insights into the efficient deployment of QWC stations and the integration of renewable energy and energy storage for sustainable urban electric bus networks. The proposed optimization models provide a systematic approach to designing and operating charging infrastructure, contributing to sustainable urban transportation systems. Moreover, the study highlights the influence of seasonal data on PV system sizing and costs.

Integrated Optimization of Route and Frequency for Rail Transit Feeder Buses under the Influence of Shared Motorcycles

In this paper, we develop a multi-objective integrated optimization method for feeder buses of rail transit based on realistic considerations. We propose a bus stop selection method that considers the influence of shared motorcycles, which can score the importance of alternative bus stops and select those with the highest scores as objectives. The objective of the model in this paper is to minimize both the travel costs of passengers and the operating costs of the bus company. This is achieved by optimizing feeder bus routes, the frequency of departures, and interchange discounts to enhance the connectivity between feeder buses and rail transit. In addition, to ensure the feasibility of generated routes in the real road network, a genetic algorithm encoded with priority is used to solve this model. We use the Xingyao Road subway station in Kunming as an example, and the results show that the optimization method is effective.

Neighborhood influence on walking acceptability to bus stops in Hanoi, Vietnam

In Asian megacities, behind wide streets accessible by car, there are areas of narrow streets that are inaccessible by bus or car. This study investigates measures to improve access to bus stops in Hanoi from the perspective of the neighborhood environment of these residents. These results are expected to contribute to solving similar problems in Asian megacities. The survey divided residents into two groups, one group living on wide streets and the other group living on narrow streets in Hanoi, then obtained and analyzed data on their attitudes towards walking to bus stops through a questionnaire survey. The results revealed the following. The distance to the bus stop was accessible for both groups. Between the two groups, the attitude towards walking to the bus stop is more influenced by the location factor than the occupation (employee or student). Factors such as the benefits of walking, infrastructure, and security conditions play an important role in the decision to walk to the bus stop during the daily commute.