Stop Locations Research Articles

Enhancing Urban Mobility with Aerial Ropeway Transit (ART): Future Accessibility Impacts of Multimodal Transit Expansion Scenarios

Aerial ropeway transit (ART) systems are emerging alternatives to augment existing transit systems in congested cities in the Global South, especially in urban areas with limited transit coverage because of road width constraints or topography. Integration of aerial cable car stations to an existing transit network can improve the overall accessibility of various population segments with significant positive benefits in relation to reducing transport-related social exclusion. This study evaluated the impact of introducing ART in the city of Varanasi (India) and assessed the spatial accessibility improvements to critical facility locations such as heritage sites, educational institutions, hospitals, and employment centers. Several multimodal transit expansion scenarios were considered in this study and the potential benefits of each case were quantified using the two-step floating catchment area (2SFCA) method. A multi-criteria decision-making (MCDM) approach was subsequently employed for identifying the optimal locations of ART stops. Microlevel analysis findings suggest that the mean accessibility values could increase up to 10.92% in the first phase of the ART implementation, which could subsequently increase to 24.7% and 49.8% for the subsequent transit expansion scenarios. The study also investigated the Varanasi ART DPR prepared by Varanasi Development Authority (VDA) and showed that a significant increase of 16% in accessibility levels could be achieved if optimal stop locations identified in this study were implemented. The proposed two-step (2SFCA+MCDM) method for identifying the optimal locations of ART stations in a multimodal transit network is expected to be an effective tool for transit system redesign using place-based accessibility measures.

Optimized demand-based charging networks for long-haul trucking in Europe

Abstract Battery electric trucks (BETs) are the most promising option for fast and large-scale CO2 emission reduction in road freight transport. Yet, the limited range and longer charging times compared to diesel trucks make long-haul BET applications challenging, so a comprehensive fast charging network for BETs is required. However, little is known about optimal truck charging locations for long-haul trucking in Europe. Here we derive optimized truck charging networks consisting of publicly accessible locations across the continent. Based on European truck traffic flow estimates for 2030 and actual truck stop locations we construct a long-term charging network that minimizes the total number of required locations. Our approach introduces an origin-destination pair sampling method and includes local capacity constraints to compute an optimized stepwise network expansion along the highest demand routes in Europe. For an electrification target of 15% BET share in long-haul and without depot charging, our results suggest that about 91% of electric long-haul truck traffic across Europe can be enabled already with a network of 1,000 locations, while 500 locations would suffice for about 50%. We furthermore show how the coverage of origin-destination flows scales with the number of locations and the size of the stations. Ideal locations to cover many truck trips are at highway intersections and along major European road freight corridors (TEN-T core network).

Hyper pooling private trips into high occupancy transit like attractive shared rides

The size of the solution space associated with the trip-matching problem has made the search for high-order ride-pooling prohibitive. We introduce hyper-pooled rides along with a method to identify them within urban demand patterns. Travellers of hyper-pooled rides walk to common pick-up points, travel with a shared vehicle along a sequence of stops and are dropped off at stops from which they walk to their destinations. While closely resembling classical mass transit, hyper-pooled rides are purely demand-driven, with itineraries (stop locations, sequences, timings) optimised for all co-travellers. For 2000 trips in Amsterdam the algorithm generated 40 hyper-pooled rides transporting 225 travellers. They would require 52.5 vehicle hours to travel solo, whereas in the hyper-pooled multi-stop rides, it is reduced sixfold to 9 vehicle hours only. This efficiency gain is made possible by achieving an average occupancy of 5.8 (and a maximum of 14) while remaining attractive for all co-travellers.

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.

To walk or not to walk? Designing intelligent order picking warehouses with collaborative robots

Order picking is a physically demanding, time-consuming, and costly process in most warehouses. To make the process more efficient, recently, ride-on autonomous robotic order pick trucks (collaborative robots or cobots) have been introduced, that assist the order picker. The order picker can ride on the cobot to travel large distances, but when picking, the cobot behaves as a robot and moves autonomously to the next stop location. The question is where the order picker should get on the cobot (step-on location) to ride further. Using traditional low-level order pick trucks, the order picker rides to every stop location and steps on the truck every time immediately after depositing the picked item on the pick pallet or roll cage. Although riding the truck may be faster than walking, stepping off/on the truck is time-consuming and also demanding for order pickers as it puts much pressure on the knee joints. The cobots allow reducing both travel time (compared to walking only) and knee flexion (compared to riding only). We determine the benefits of choosing optimal step-on locations by formulating an optimization model to minimize total time, including a penalty on the number of knee flexes of the order picker. Since the problem is computationally intractable for large-sized problems, we propose a dynamic programming approach which finds the shortest path of subproblems in each aisle. We find that the optimal collaboration strategy will decrease total travel time, as well as knee flexion of the order picker. Based on Monte Carlo simulation, our results indicate time savings up to 27.9% for one-block and 26.5% for two-block warehouses compared to a heuristic from practice. Based on the data and working practice we obtained from a retail warehouse, the optimal collaboration strategy can improve current practice between 14.5% and 24.1%.

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.

Urban Infrastructure Construction Planning: Urban Public Transport Line Formulation

Urban public transport line formulation has its appeal in promoting public convenience and developing environmentally friendly cities. During the bus line planning stage, the line frequency and stop location determination is a key issue for decision makers. Our study focuses on the integrated formulation problem between line frequency and stop planning featuring multi-type vehicles. The multi-type vehicles are able to accommodate the various passenger demands at either peak hours or off-peak hours. The a priori magnitudes of user demands are investigated by drone-based technique methods in the tactical-level plan. The collected geospatial data can assist the public transport user forecast. A mixed-integer linear programming (MILP) model is proposed. The objective is to minimize the walking cost of passengers, the building cost of stops, and the operation cost of service frequency. The effectiveness of the model is validated by a real case in Nantong, China. CPLEX is used to resolve the MILP model. Yielding to the budget constraint, in high-price, medium-price, and low-price scenarios, the optimal high-quantity stop scheme can save 3.04%, 3.11%, and 3.38% in overall cost compared with the medium-quantity stop scheme, respectively; their cost savings are 8.53%, 8.70%, and 9.09% more than the costs of the low-quantity stop scheme.

Study on Factors of Urban Transportation Service Quality Based on Analytic Hierarchy Process: A Case Study of Gangtok City

Promoting the use of public transportation requires an in - depth knowledge of the variables that affect the choice of public transportation mode, including the process of implementing policy, as it has become a crucial component of government strategies to reduce carbon emissions from the transportation sector globally. A variety of factors impacting the usage of public transportation have been identified in previous studies, but few have been included in a single study. In order to boost the use of public transportation, it is essential to make it more attractive to travellers by conducting frequent Service Quality (SQ) assessments and adjustments. Understanding the passengers’ expectations towards public transportation are important, and assessing the SQ is an important tool for evaluating the total performance of the Gangtok’s public transportation system. The purpose of the existing study was to study the expectations and perceptions of city’s passengers regarding SQ in public transportation. In order to comprehend how the present commuters’ prefer a particular service quality attribute a series of interviews with passengers were undertaken. MCDM technique was adopted for this. The paper focuses on service quality factors for urban transportation system by adopting AHP technique. In this study perception of passengers using public transport in Gangtok is taken from the field. The case study is taken for Gangtok city in the state of Sikkim. The study involves study of passengers from various bus stop locations of the city. The results of the research showed substantial discrepancies among expectations and perceptions of city’s commuters, along with widespread dissatisfaction with the delivery of public transit services in hilly areas as a whole. The reliability and adaptiveness of these service have been critical in describing the overall quality of transportation services in hilly areas, and best practices were used to develop a set of recommendations for transportation operators and local officials.

Real-time bus arrival delays analysis using seemingly unrelated regression model

AbstractTo effectively manage and control public transport operations, understanding the various factors that impact bus arrival delays is crucial. However, limited research has focused on a comprehensive analysis of bus delay factors, often relying on single-step delay prediction models that are unable to account for the heterogeneous impacts of spatiotemporal factors along the bus route. To analyze the heterogeneous impact of bus arrival delay factors, the paper proposes a set of regression equations conditional on the bus location. A seemingly unrelated regression equation (SURE) model is developed to estimate the regression coefficients, accounting for potential correlations between regression residuals caused by shared unobserved factors among equations. The model is validated using bus operations data from Stockholm, Sweden. The results highlight the importance of developing stop-specific bus arrival delay models to understand the heterogeneous impact of explanatory variables. The significant factors impacting bus arrival delays are primarily associated with bus operations, such as delays at consecutive upstream stops, dwell time, scheduled travel time, recurrent congestion, and current traffic conditions. Factors like the calendar and weather have significant but marginal impacts on arrival delays. The study suggests that different bus operating management strategies, such as schedule adjustments, route optimization, and real-time monitoring and control, should be tailored to the characteristics of stop sections since the impacts of these factors vary depending on the stop location.

Conflict management at urban bus stops through cooperative trajectory planning

Conflicts due to frequent bus dwelling and entering/exiting maneuvers at bus stops have significantly undermined the efficiency, safety, and sustainability of the urban bus system. This paper proposes a cooperative trajectory planning strategy in a Vehicle-to-Infrastructure (V2I) environment for conflict management between buses and cars at bus stops. A dynamic multi-stage-based model along with solution algorithm is developed to search trajectories that eliminate the conflicts between cars and buses entering and leaving the bus stop in real time.The assessment of the proposed strategy is carried out at both the bus stop and corridor levels within a simulated environment. In-depth trajectory analyses during a typical weaving process at a bus stop reveal that the proposed strategy effectively eliminates bottlenecks and enhances operational performance across multiple approach lanes affected by the bus stop. A thorough evaluation involving multiple cycles of weaving among buses and vehicles at the bus stop reaffirms the strategy’s effectiveness under varying levels of bus occupancies, traffic demand, and bus stop locations. The case study at the corridor level illustrates that the proposed strategy successfully improves operational performance and bus schedule adherence across diverse scenarios, encompassing different roadway link lengths, traffic demands, bus headways and dwelling times, and bus stop locations. It notably excels in enhancing the performance of an urban corridor with longer link distances and near-side located bus stops, particularly under high traffic demand levels and shorter bus dwelling times.

Heterogeneous impacts of the built environment on survival of customized bus services across the downtown and suburbs

Customized bus services (CBS) lines have been increasingly added in cities to better serve diversified travel needs. The notable variations in the built environment across the downtown and suburbs could result in wide differences in local customers' behaviors and preferences. The allocation of CBS stops thus needs to be considered separately in urban centers and peripheries to ensure the best performance, but such spatial heterogeneity is not specifically studied and justified in the literature. This study takes empirical data containing continuous CBS stops in Shanghai, China, as an example. Using a latent class binomial logit model, it investigates the effects of bus characteristics, geographic, and built environment elements on the survival of CBS stops in downtown and suburbs. The findings demonstrate that the performance of CBS systems is significantly influenced by factors including road density, population density, POI, sidewalk width, greenery area, ticket fare, and the type of CBS line (commute or general) across urban centers and peripheries. Specifically, two latent classes of CBS stops are found to have different effects depending on the stop's location or type of the linked areas, i.e. downtown or suburbs, on a CBS line.

Quantum entanglement and self-attention neural networks: An investigation into passengers and stops characteristics for optimal bus stop localization

Urban transportation significantly contributes to global carbon emissions, thus emphasizing the importance of green and efficient alternatives like public transportation. Enhancing the appeal of public transport through the strategic placement of bus stops can mitigate carbon emissions concurrently. This research proposes a Quantum Entangled Self-Attention Neural Networks (QESANN) model that considers both the passengers and stops characteristics to optimize bus stop locations. The QESANN model integrates the complexity of the relationship between passengers and bus stops characteristics, thereby fostering a more holistic approach to bus stop placement. It uses a Conditional Random Field (CRF) to initialize and process the attributes of both commuters and bus stops through quantum entanglement. The Vehicle Specific Power (VSP) model is employed to gage the variation in bus carbon emissions pre and post bus stop location optimization. Simulation tests, conducted via the Anylogic software and the IBM Quantum platform, demonstrated promising results. Over a yearly travel cycle for 1,000 individuals, the number of bus commuters rose from 322 to 651, concurrently reducing urban traffic carbon emissions by 430,243.70 kg annually. These findings affirm the effectiveness of the proposed QESANN model in enhancing the appeal of bus stops, reducing carbon emissions from urban transportation, and fostering a more sustainable urban environment.

A spatial statistical approach to estimate bus stop demand using GIS-processed data

This study integrates the fields of geography, urban transit planning, and statistical learning to develop a sophisticated methodology for predicting bus demand at the stop level. It uses a Generalized Additive Model that captures non-linear relationships and incorporates spatial dependence, improving traditional methods. It showcases a high predictive capacity with a pseudo R-squared of 0.79 during its validation, ensuring substantial explanatory power for new observations. A large number of variables, including land-use characteristics, socioeconomic factors, and transit supply, are analysed. These widely available predictors facilitate the transferability of the methodology to other urban areas. Transit supply predictor considers the number of annual trips per stop and area as well as the location of stops along the lines that serve them. GIS processing of the data allows the calculation of variables within the areas of influence of each stop, obtained by following the walkable street network. For the case study, the presence of universities, hospitals, and lodgings areas, as well as inhabitants and ratio of bus trips show a positive impact on bus demand. This geo-analysis process employs accurate disaggregated data, such as information on uses in each building, as well as methods for assigning socioeconomic information from local areas to residential buildings. This study highlights the complex relationship between the location of transit network stops, both along the bus line and in terms of geographical proximity, their transit supply, and its surrounding factors. The results indicate that there is spatial dependence for stops less than 1.15 km apart. The developed methodology provides reliable information to transit network planners for decision making. Specifically, this proposed methodology can contribute to designing new routes, optimizing stop locations, and estimating the impact of changes in the transit network or urban planning on bus demand. All these improvement measures promote sustainable urban mobility, consequently fostering environmental and social benefits.

BUS KARO!: Live Bus Tracking System

Abstract: This research paper introduces 'BusKaro!', a transformative solution aimed at enhancing public transportation accessibility and promoting environmental sustainability. In the context of pressing global concerns regarding climate change and pollution, the imperative to transition from private vehicles to public transport is underscored. However, the efficiency and reliability of public transportation systems, particularly in densely populated urban areas, remain significant challenges. 'BusKaro!' addresses these challenges by leveraging advanced GPS technology to provide real-time tracking of buses, enabling users to access accurate information about bus locations and schedules. Beyond mere convenience, 'BusKaro!' offers a comprehensive platform that empowers commuters with features such as route planning, nearby bus stop location, and service disruption alerts. Moreover, 'BusKaro!' serves as a catalyst for broader societal change. By promoting the use of public transport, it contributes to reducing traffic congestion, lowering carbon emissions, and fostering sustainable urban environments. Through a detailed examination of its design, implementation, and potential impacts, this paper elucidates the transformative potential of 'BusKaro!' in shaping the future of transportation systems. Case studies and analysis further highlight the tangible benefits of adopting 'BusKaro!' for cities and commuters alike, illustrating its role as a pivotal tool in the journey towards a more sustainable future.

Does it Matter Where? Evaluating the Spatial Heterogeneity of Police Post-Stop Enforcement

Scholars know relatively little about how the location of a pedestrian police stop affects the racial distribution of post-stop outcomes, including the initiation of a search or a field interview. To address this gap, this research draws on a unique data set from San Jose, California, and underutilized spatial methods to examine the extent to which conflict theory explains post-stop enforcement patterns. We consider two iterations of the theory: (1) the racial threat hypothesis, which posits that Blacks and Hispanics are more aggressively policed in minority neighborhoods, and (2) the racial incongruity hypothesis, which holds that police tend to target minorities occupying concentrated White spaces. Hierarchical linear modeling (HLM) models indicate that Blacks and Hispanics face more aggressive post-stop enforcement than Whites but that stop location is not predictive. By contrast, geographically weighted regression (GWR) analysis shows that police are more likely to initiate searches and field interviews of Blacks and Hispanics in areas with high concentrations of White and minority residents. These findings illustrate the nuanced relationship between stop location, pedestrian race, and police behavior. Conflict theory is a valuable lens through which to view post-stop enforcement, yet evidence to support the minority threat and racial incongruity hypotheses was only visible at the micro level. This research adds to existing scholarship by demonstrating the utility of GWR in teasing out the nuanced, micro-level relationship between stop location and pedestrian race not captured in more traditional models.

Enhancing school to public transportation accessibility in Solo City: A comprehensive assessment of Batik Solo Trans (BST) bus stop locations

This study primarily aims to evaluate the affordability of Batik Solo Trans (BST) bus stops to school locations within Solo City and to develop a strategic placement plan for enhancing student accessibility throughout the city. The criteria for optimal placement adhere to the Minister of Public Works Regulations, which dictate that bus stops should be within a 400-meter radius or a 10-minute travel time for pedestrians. The research employs a comprehensive methodology, including a census of existing BST bus stops and high schools in Solo City. Using ArcGIS 10.2, we employ Buffer Proximity Analysis and Network Analysis to identify potential new bus stop sites, followed by quantitative and descriptive analysis of the results. The study’s findings reveal that BST bus stop accessibility to high schools in Solo City stands at an impressive 94%, with 350 out of 175 high schools having convenient access to 165 BST bus stop locations. In order to address accessibility gaps, the research suggests integrating 16 additional bus stops, especially at notable high school locations such as SMP Muhammadiyah PK Campus II, SMPN 18 Surakarta, and MAN 1 Surakarta. The proposed strategy aims to achieve full coverage with enhanced accessibility, visualized through cartographic representation.

Empirical Investigation of Dwell Times in Ridepooling Systems

While ridepooling has garnered considerable attention in research, there remains a notable gap in understanding the intricacies of the dwelling process, particularly the boarding and alighting of passengers. Existing studies have predominantly focused on dwelling behavior within conventional transportation modes like buses or trams. This study seeks to fill this gap by providing insights into passengers’ boarding and alighting behavior within ridepooling systems and examining the factors influencing dwell time, such as group size and stop location. To achieve this, we collaborated with sprinti, an on-demand ridepooling service in the Hanover region, Germany. We collected data on dwell times and customer characteristics during several shifts in 2022 by employing a ‘silent observer’ approach. The findings reveal an average dwell time of 16.25 s and a median of 13 s. Additionally, the analysis demonstrates that several determinants impact dwell time, such as group size, vehicle type, and the presence of luggage.

Minimization of metabolic energy expenditure in collaborative order picking

Order picking is one of the most repetitive, labor-intensive, and physically demanding operations in warehouses. Picking hundreds of orders daily requires high metabolic energy expenditure and is characterized by poor ergonomics posing high risks for musculoskeletal disorders. In traditional order picking, the order picker walks around racks in a warehouse throughout the day. Alternatively, it is aimed at minimizing inefficient time and musculoskeletal strains with ride-on order picking by allowing the order picker to stand on an operator’s platform of an order-picking truck and ride the truck between stop locations. However, the order picker has to step down from the platform at each stop location and step up onto the platform before riding the truck to the next stop location. Therefore, riding the truck with frequent stops leads to more metabolic energy expenditure and musculoskeletal disorders than walking, although it is faster. Benefiting advantages of both traditional and ride-on order picking, a relatively new order picking truck (collaborative order picking truck) is deployed in warehouses to reduce inefficient walking time and ergonomic riding disorders. In collaborative order picking, the order picker can walk from a stop location directly to the next pick location while the truck moves to the next stop location autonomously or ride the truck to the next stop location in case of having a large distance between stop locations. This paper develops an optimization model to minimize total metabolic energy expenditure in collaborative order picking by finding the shortest route and the best collaboration decision (walk or ride). Based on the Monte Carlo simulation, the metabolic energy expenditure with collaborative order picking is analyzed. Our results indicate metabolic energy savings with collaborative order picking up to 200% and 83% compared to traditional and ride-on order picking, respectively.

Integrating Urban Air Mobility into a Public Transit System: A GIS-Based Approach to Identify Candidate Locations for Vertiports

Stakeholders expect emerging urban air mobility (UAM) services that use electric vertical takeoff and landing (eVTOL) aircraft to revolutionize transportation systems. However, to be effective, eVTOL facilities, known as vertiports, must seamlessly integrate with the existing multimodal transportation infrastructure. This research analyzes how to best integrate vertiports with the existing public transit network of a city, with San Francisco in the United States as a case study. This study developed a composite optimization model to identify the locations for an optimum number of vertiports within each of the eleven (11) supervisorial districts of San Francisco that would minimize the network distance to its bus stops. The results revealed that 38 locations for vertiports covered 3245 bus stops with an average walk time close to 10 min. Walking 10 min from each vertiport would cover 41.6% of the total road network length and 49.8% of all bus stop locations. Power-law regressions of the average distances as a function of the number of vertiports will help planners to make informed decisions about the optimal vertiport placements in each district by identifying diminishing returns in travel-time savings, and adjustments that consider income and population as additional demand factors.