Stop Placement Research Articles

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.

Optimization Method for Conventional Bus Stop Placement and the Bus Line Network Based on the Voronoi Diagram

With the rapid development of the economy, the existing conventional bus transit system finds it difficult to meet people’s increasing travel needs. In addition, with the emergence and rapid development of urban rail transit, it is also necessary to integrate the existing conventional bus transit system with the rail transit system to realize the optimization of the whole public transport system. This study introduces the concept of the Voronoi diagram and uses it to divide the service area of bus stops. Taking the average walking time of regional passengers to the station as the main index, the convenience of passengers in the service area was evaluated, and a set of candidate station sites is established. Against the background of urban rail transit, a complete optimization model for a conventional bus station layout and line network was proposed. Finally, taking Wuhan East Lake High-tech Development Zone as an example, two optimization schemes for the public transport system were obtained. Compared with the status quo, the optimized scheme had obvious improvement effects on the repetition coefficient of bus lines, per capita transfer time, bus line network coverage and station service rate. This has been recognized by the local authorities, which proves the practicality and scientificity of the optimization method of this study.

Inferring the solution space of microscope objective lenses using deep learning

Lens design extrapolation (LDE) is a data-driven approach to optical design that aims to generate new optical systems inspired by reference designs. Here, we build on a deep learning-enabled LDE framework with the aim of generating a significant variety of microscope objective lenses (MOLs) that are similar in structure to the reference MOLs, but with varied sequences-defined as a particular arrangement of glass elements, air gaps, and aperture stop placement. We first formulate LDE as a one-to-many problem-specifically, generating varied lenses for any set of specifications and lens sequence. Next, by quantifying the structure of a MOL from the slopes of its marginal ray, we improve the training objective to capture the structures of the reference MOLs (e.g., Double-Gauss, Lister, retrofocus, etc.). From only 34 reference MOLs, we generate designs across 7432 lens sequences and show that the inferred designs accurately capture the structural diversity and performance of the dataset. Our contribution answers two current challenges of the LDE framework: incorporating a meaningful one-to-many mapping, and successfully extrapolating to lens sequences unseen in the dataset-a problem much harder than the one of extrapolating to new specifications.

Robust Optimization of Bus Stop Placement Based on Dynamic Demand Using Meta Heuristic Approaches: A Case Study in a Developing Country

Abstract The operating condition of bus transit system has not been efficient in most cities of Iran, and many management methods such as regular bus scheduling, assigning exclusive bus lanes, etc., which are necessary for increasing the efficiency of this system, were not regarded enough. Thus, achieving a method for locating the bus stops and optimizing the number of such stops based on a non-homogeneous spatial and temporal distribution of passengers as well as the local traffic patterns are important to be investigated. As such, the present study aims to investigate the modeling of a bus transit system corridor according to the non-homogeneous spatial and temporal distribution of passengers throughout the route aiming at optimization of the number of attracted passengers to the bus. For this purpose, the 8-km route from Vali-e-asr roundabout to Gas roundabout in the city of Rasht in the north of Iran is selected for modeling. Hammersley sampling method, as well as two heuristic optimization techniques, including a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm, are used for generating a non-uniform population and solving the optimization model. Therefore, the results of this analysis are compared to the optimization results by using the probabilistic analysis without considering the reference uncertainty. Finally, the PSO is selected as the superior algorithm for modeling and locating the bus stops due to its results in less travel time, and the validity of robust optimization model is shown due to its higher accuracy and adaptation to the real-world environment. Overall, although the optimization results based on indeterminate analysis in comparison to determinate analysis brought about more average travel time, more population sets were covered by the new introduced stops during 18 active hours of the bus transit system.

Willingness To Pay For Improving The Quality Of Public Transportation

This study aims to know the magnitude of the estimated average value of willingness to pay service users BRT Trans Semarang to improve the service quality and to know the change of the desired service quality improvement from the users of BRT Trans Semarang. Quantitative descriptive research approach with Contingent Valuation Method (CVM) is used to analysis the data from 100 respondents. Sampling techniques are carried out by means of purposive random sampling. The finding shows that the magnitude of the average value of willingness to pay IDR. 4,000 and quality service that needs to be fixed are a convenient bus stop placement, more emphasis on user safety, timeliness the potential as well as the necessity to depart and the priority age of BRT users. Government can review back the tariff policy of BRT Trans Semarang set appropriate response from the respondents because the estimated average value of WTP can be the basic reference point for setting the rates and can improve the quality of service to make it more adequate so that users feel at ease when using the BRT Trans Semarang

Analysis of Cooperative Bus Priority at Traffic Signals

Transit signal priority (TSP) is an effective strategy to control bus schedule adherence, increase ride comfort, and, in turn, reduce fuel consumption. However, its provision may cause negative externalities on general traffic. In this paper, we demonstrate how coordinated in-vehicle driver advisory systems (DASs) can partly reduce the signal priority control requests. Such cooperative solution, enabled by the connectivity between vehicles and the signal control system, allows combining signal adjustments with dwelling and speed advisories. We show that combining in-vehicle advisory with signal priority can reduce stops at signals, decrease trip time when compared with TSP used alone and, in some case, achieve the same performance without resorting to TSP. In addition, we demonstrate that DASs used alone have marginal impact on trip times, hence can be applied unconditionally. A simulation-based analysis indicates that the effectiveness of a control strategy—or a combination of different strategies—depends on the bus stop placement. When it comes to reduction of stop frequency at traffic signals, speed and dwell time advisories used together deliver similar performance to an unconditional TSP with green signal extensions and recalls lasting up to 10 s.

Do Rent Increases Reduce the Housing Supply Under Rent Control? Evidence from Evictions in San Francisco

Rent control balances strong tenant protections with supply-side incentives for landlords. However, cities with rent control are also some of the United States' most unaffordable, prompting questions about how well these incentives are working. I examine how controlled landlords change their housing supply in response to price increases using a well-identified hyperlocal demand shock the privately operated commuter shuttle systems in San Francisco. Controlled landlords increased market withdrawal filings and became less likely to create vacancies via evictions in response to a shuttle stop placement. Policies raising barriers to market withdrawals prompted controlled landlords to respond my increasing their at-fault evictions.

Network-level Optimization of Bus Stop Placement in Urban Areas

Bus stops provide accessibility to public transit service, whereas they also influence the efficiency of mobility due to the extra dwell time. The decision of bus stop locations is a tradeoff between access coverage and mobility. This paper formulates a biobjective optimization model of bus stop placement problem at the network level. Two objectives are pertinent to the improvement of mobility: minimization of total dwell time at stops and minimization of total number of bus stops. Access coverage is constrained to ensure a certain level of accessibility. The issue of stop congestion and its effect on road traffic flow is also considered in the model. The model is applied to a case study of the bus network in urban areas of Yancheng, China. The results show that the proposed model can be efficiently solved by CPLEX to obtain Pareto optimal solutions for real-case problems.

Should Optimal Stop Spacing Vary by Land Use Type?: New Methodology

This study aimed to establish whether optimal stop spacing should vary by land use type. A new stop clustering method was proposed with stops divided into catchments on the basis of land use. A bilevel optimization model was then used to suggest optimal stop spacing for these catchments. The upper-level problem minimized a cost function that represented the perspectives of the passengers, the operators, and the local authority, and the lower-level problem was the transit assignment problem. Results show that stop spacing in the catchments with residential usage is generally larger than in those catchments with nonresidential or mixed land use. Near-optimal solutions were proposed to accommodate local considerations of stop placement. The spacing interval created by near-optimal solutions could be used to adjust existing stop placement. A case study of Tram Route 109 in Melbourne, Australia, revealed that the practical implication of the proposed analytical approach indeed improved the practical operations of the tram service. In future studies, the model could be extended to a network of tram routes. Overall, the results suggest that optimal stop spacing on public transport should be a function of land use type.

Optimizing School Bus Stop Placement in Howard County, Maryland

In the current system, school bus stops in Howard County, Maryland are manually placed along the school bus routes based on safety, cost-efficiency, and many other variables. With such liberal placement, bus stops are sometimes placed unnecessarily. This issue is prevalent in many school districts and often results in needlessly close bus stop proximity. In this study, the authors implemented a GIS-based heuristic to assist school officials in optimizing their districts bus stop placement. They also estimated the proportion of county-wide bus stops that could be eliminated by this approach. Following the constraints determined by State and local guidelines, the ArcGIS Network Analyst Extension was used to identify unnecessary bus stops across the study area. The initial output was re-evaluated by school officials in order to determine if those bus stops would be eliminated. The results indicate that approximately 30% of the existing bus stops were marked as “candidates for elimination” by the GIS process. After a review of these candidates, it was determined that at least 15% of the total school bus stops could be eliminated. Statistical estimates lent credence to the benefit of a re-evaluation of these bus stops. The method developed in this study can easily be replicated. Hence, it may inspire other school systems to exercise the same approach. Additionally, the results provide a gateway for future studies in examining more efficient school bus routes with less travel time, as well as investigating how much the carbon footprint of school bus fleets can be reduced.

Optimization of bus stop placement for routes on uneven topography

The improvement and expansion of public transport is an increasingly important solution to the high congestion costs and worsening environmental impacts of the car dominated transport systems seen in many cities today. The intelligent design of stop locations is one way to improve the quality of PT and thereby improve its ridership. Stop placement is a relatively complex task as it involves a trade-off between two competing goals; accessibility and operation; however this trade-off can be made explicit using an appropriate mathematical model. Many such models have been developed in the literature, however none consider the effects of uneven topography. Topography is an important but often neglected factor in the design of public transportation systems, with the potential to have a significant impact on the accessibility, operation and planning of a transit service. In this work a mathematical modelling approach to bus stop placement is developed which includes considerations of uneven topography in three ways; (1) Its effect on walking speed; (2) Its impact on the attractiveness of an access path to a transit service; and (3) Its effect on acceleration rates at stops. Because of the complexity of the model developed, a heuristic evolutionary algorithm’ is employed to approximate an optimal solution to the model. Finally, the model and solution method are applied to a case study in the Auckland CBD area in New Zealand.

Bi-Level Programming of Urban Bus Stop Selection

By using the system analysis method. and game theory of governmental placement of urban bus stops, operation of transit enterprises, and travelers' choice of trip mode, transit line, and bus stop location, the research objectives of this study are to solve the bi-Ievel programming problem for optimized urban bus stop placement with elastic demand and to establish a bi­ level programming model. The upper-level goal of the government and transit enterprises for choosing bus stops from possible se­ lections is to minimize travelers'total travel cost, to minimize bus operating cost, and to maximize the bus trip volume. The low­ er-level goal of the traveler for choosing travel mode, transit lines, and final bus stop is to minimize his generalized cost within equilibrium of the traffic assignment. A genetic algorithm is adopted in the concrete optimization solution. The game analysis of different decision agents and the concrete restriction of bus stop location are considered using the bi-Ievel programming method for ultimate selection of the bus stops. Moreover, the feasibility and correctness of the method. are proved.

Piloting Low-Cost Transit Service Enhancements through Agency Collaboration

In 2011, the City of Philadelphia, Pennsylvania; the Southeastern Pennsylvania Transportation Authority (SEPTA); and the Delaware Valley Regional Planning Commission (DVRPC) launched the Route 47 Service Enhancement Pilot Project. This project was conceived within the framework of the Transit First Committee. The pilot presented an opportunity to evaluate the effectiveness of low-cost operational initiatives that might have the ability to improve transit services in dense, urban environments. These strategies included bus stop consolidation, farside stop placement, and the switch from a time point–based scheduling system to a headway-based scheduling system. This joint initiative served as a test of the potential to build community support for a pilot, the impact of the interventions themselves, and the ability of the Transit First Committee to work together collaboratively. Although the pilot initiative did not deliver the time savings expected, the process provided important lessons for the city, SEPTA, and DVRPC that are applicable for other transit agencies operating in similar geographic, political, and economic environments. The results of the collaborative process necessary to get the pilot in motion helped to reconnect the city and SEPTA after years of distrust and discord. The initiatives piloted were scrutinized through data analysis, which provided not only an understanding of the impacts but also an awareness of operational performance issues. Ultimately, this extensive data analysis supported decision making, fostered agreement between stakeholders, and developed trust within the community.

Bus stops and pedestrian–motor vehicle collisions in Lima, Peru: a matched case–control study

ObjectiveTo evaluate the relationship between bus stop characteristics and pedestrian–motor vehicle collisions.MethodsThis was a matched case–control study where the units of study were pedestrian crossings in Lima, Peru. We performed...

Incorporating crowding into the San Francisco activity-based travel model

Information produced by travel demand models plays a large role decision making in many metropolitan areas, and San Francisco is no exception. Being a transit first city, one of the most common uses for San Francisco’s travel model SF-CHAMP is to analyze transit demand under various circumstances. SF-CHAMP v 4.1 (Harold) is able to capture the effects of several aspects of transit provision including headways, stop placement, and travel time. However, unlike how auto level of service in a user equilibrium traffic assignment is responsive to roadway capacity, SF-CHAMP Harold is unable to capture any benefit related to capacity expansion, crowding’s effect on travel time nor or any of the real-life true capacity limitations. The failure to represent these elements of transit travel has led to significant discrepancies between model estimates and actual ridership. Additionally it does not allow decision-makers to test the effects of policies or investments that increase the capacity of a given transit service. This paper presents the framework adopted into a more recent version of SF-CHAMP (Fury) to represent transit capacity and crowding within the constraints of our current modeling software.

An exploratory study of boarding home sanctions and compliance in Washington State

States vary in enforcement systems that monitor the quality of care in residential boarding homes. The growing number of people seeking long-term care services in boarding homes requires regulatory systems that are effective in quality assurance enforcement. This 6 year retrospective study describes the characteristics of 601 sanctioned and nonsanctioned homes in the state of Washington and evaluates the effectiveness of enforcement actions such as intermediate sanctions on future boarding home compliance. The intermediate sanctions evaluated are stop placement of admissions, civil fines, and conditions placed on licenses. Boarding homes that were sanctioned tended to be homes that were for-profit and had governmental contracts for Medicaid services. Homes that remained sanctioned throughout the 6 year study tended to be homes that were individual ownership corporations, had smaller numbers of licensed beds, and did not provide nursing services. Intermediate sanctions were found to vary in effectiveness. Conditions placed on licenses were the most effective intermediate sanction, and civil fines the least effective. Higher citation numbers and the most severe level of complaint types were found to be predictors of becoming a sanctioned boarding home.

A Novel Technique of Intra-Spinous Process Injection of PMMA to Augment the Strength of an Inter-Spinous Process Device Such as the X STOP

Biomechanical. To determine if cement injection into the spinous process will improve compression strength. The X STOP (St. Francis Medical Technologies) has been shown to be a safe and effective means for decompressing 1- or 2-level lumbar spinal stenosis (LSS). The X STOP is indicated for LSS patients with osteoporosis, but contraindicated for patients with severe osteoporosis. In an attempt to address these LSS patients with demonstrably weaker bone, a technique to strengthen the spinous process with polymethylmethacrylate (PMMA) injection is presented. Nine pairs of adjacent fresh frozen cadaveric lumbar vertebrae were DEXA scanned before testing. They were randomly assigned to the PMMA group and a control group. Nine of the specimens were injected with PMMA. Each spinous process was then compressed between 2 X STOPs. The testing model was designed to simulate the loading of a 2-level X STOP placement. The mean load to failure and stiffness values of the treated and untreated groups were calculated. The specimens were inspected carefully for PMMA infiltration and extrusion. The mean bone mineral density (BMD) values of the control and PMMA treatment groups were 0.99 +/- 0.13 g/cm and 0.98 +/- 0.10 g/cm, respectively; P > 0.616. The mean volume of cement injected was 2.2 +/- 0.3 cc. The mean failure load values of the control and PMMA treatment groups were 1250 +/- 627 N and 2386 +/- 1034 N, respectively; P < 0.001. The mean stiffness values of the control and PMMA treatment groups were 296 +/- 139 N/mm and 381 +/- 131 N/mm, respectively; P > 0.059. Most specimens had flow of the cement into the laminae and some into the facet and pedicle. No PMMA was found within the spinal canal. This first reported technique of posterior element vertebroplasty may increase the indications and success for patients with decreased BMD who seek an interspinous implant such as the X STOP. A possible role may exist in increasing the effectiveness of such devices. However, clinical trials have yet been performed. These results demonstrate that PMMA injection in the spinous processes is effective in increasing resistance to compressive forces in an X STOP model.

Near Side, Far Side, Uphill, Downhill: Impact of Bus Stop Location on Bus Delay

One of the factors affecting where bus stops should be located is the expected delay associated with the stop location. On hills, the effect of gravity on already weak diesel engines can lead to considerable additional delay if a bus has to accelerate from a stop. An empirical bus acceleration profile, modified to account for gravity, was applied to constant grade, sag curve, and crest curve profiles. The marginal impact of grade on stopping delay ranged from -4 to 11 s, depending on grade. At signalized intersections, a deterministic model was created that accounted for deceleration, acceleration, and queue interference. Relative to a stop placed away from an intersection, far-side stop placement either causes a very small reduction in delay or has no effect. Near-side placement can reduce delay in a few cases such as reserved bus lanes, but more often it increases delay, sometimes considerably depending on factors such as red ratio, volume:capacity ratio, cycle length, and stop setback. Measures that re...

Near Side, Far Side, Uphill, Downhill

One of the factors affecting where bus stops should be located is the expected delay associated with the stop location. On hills, the effect of gravity on already weak diesel engines can lead to considerable additional delay if a bus has to accelerate from a stop. An empirical bus acceleration profile, modified to account for gravity, was applied to constant grade, sag curve, and crest curve profiles. The marginal impact of grade on stopping delay ranged from –4 to 11 s, depending on grade. At signalized intersections, a deterministic model was created that accounted for deceleration, acceleration, and queue interference. Relative to a stop placed away from an intersection, far-side stop placement either causes a very small reduction in delay or has no effect. Near-side placement can reduce delay in a few cases such as reserved bus lanes, but more often it increases delay, sometimes considerably depending on factors such as red ratio, volume:capacity ratio, cycle length, and stop setback. Measures that reduce interference with the queue tend to reduce the net delay from a near-side location; these measures include increasing stop setback, shortening cycle length, and giving the bus a (near) exclusive lane. Results are presented with default adjustments for hills and signalized intersections that can be used in the context of a stop spacing study.

Shelter from the Storm: Optimizing Distribution of Bus Stop Shelters in Los Angeles

The functions of bus stop shelters and factors affecting their placement at stops in transit systems are analyzed. Drawing on information from a variety of sources, current shelter placement policy in Los Angeles was found to be guided principally by the revenue-generating potential of shelter advertisements, secondarily by political concerns over geographic equity, and only peripherally on the basis of bus stop use. Using data on shelter and stop locations, boardings, and headways, a methodology is developed for measuring the cumulative use of bus stops with regard to person-min of wait time. Then this measure is used to evaluate three scenarios of bus stop placement, each of which optimizes the goals of ( a) private shelter providers, ( b) locally elected officials, and ( c) bus patrons, respectively. The conclusion is that either of the latter two scenarios would dramatically—by 2.3 person-years each day—increase the time that bus patrons in Los Angeles spend under shelter while waiting for buses at stops. This analysis both demonstrates the utility of using stop-coded boarding data in combination with headway data in the planning of bus stop shelters and shows the ineffectiveness and inequities that can arise when the sheltering of waiting passengers is not explicitly incorporated into policies guiding the placement of transit shelters.