A full factorial sensitivity analysis for a capacitated Flex-Route Transit system

El objetivo de la investigación fue analizar el sistema de transporte urbano de la ciudad de Puno, se propone de infraestructura vial urbano para tener una capacidad vial de acuerdo al tipo de vía y así poder optimizar el tiempo de viaje del pasajero según la tendencia de movilidad, movilidad de origen destino según zonas, movilidad del transporte urbano. Se analizó el recorrido de rutas de transporte público urbano para determinar la velocidad promedio y así poder conocer los tiempos de viajes. La hipótesis que se ha formulado es: con el análisis del sistema de transporte urbano se propone infraestructura vial urbana para optimizar el tiempo de viaje del pasajero de la ciudad de Puno-2018, se analiza la tendencia de movilidad según origen destino, la movilidad del transporte urbano, rutas de transporte urbano público e infraestructura vial urbana. La investigación centró su metodología, bajo un enfoque cuantitativo, diseño no experimental. Se aplicó un cuestionario como un instrumento para el análisis; de los cuales permitieron recoger información y analizar las variables de estudio y efectuar las correlaciones. La muestra estuvo conformada de 384 pasajeros y 8 microbuses que brindan servicio en las principales rutas de transporte público urbano. Como resultado se tiene que el sistema de transporte urbano de la ciudad de Puno es concentrado debido al uso de suelo en la zona céntrica, la movilidad vehicular tiene obstáculos en el flujo debido al uso de la calzada como estacionamiento y que las vías superan su capacidad vial la cual no optimiza los tiempos de viajes en ciudad de Puno para lo cual se requiere de infraestructura vial urbana como son: terminales terrestres, pavimentado de las vías paraderos, declarar zonas rígidas las vías que son utilizadas por las rutas de transporte urbano e implementar un sistema inteligente de gestión de tráfico en las intersecciones principales de la ciudad.

A survey of Flex-Route Transit problem and its link with Vehicle Routing Problem

Prelims

We develop bounds on the maximum longitudinal velocity to evaluate the performance and help the design of mobility allowance shuttle transit (MAST) services. MAST is a new concept in transportation that merges the flexibility of demand responsive transit (DRT) systems with the low-cost operability of fixed-route bus systems. A MAST system allows buses to deviate from the fixed path so that customers within the service area may be picked up or dropped off at their desired locations. However, the main purpose of these services should still be to transport customers along a primary direction. The velocity along this direction should remain above a minimum threshold value to maintain the service attractive to customers. We use continuous approximations to compute lower and upper bounds. The resulting narrow gap between them under realistic operating conditions allows us to evaluate the service in terms of velocity and capacity versus demand. The results show that a two-vehicle system, with selected widths of the service area of 0.5 miles and 1 mile, is able to serve, respectively, a demand of at least 10 and 7 customers per longitudinal mile of the service area while maintaining a reasonable forward progression velocity of about 10 miles/hour. The relationships obtained can be helpful in the design of MAST systems to set the main parameters of the service, such as slack time and headway.

Urban bus network of priority lanes: A combined multi-objective, multi-criteria and group decision-making approach

Flex-route transit service is a promising option to serve low-density urban areas. In this paper, we investigate the feasibility and benefits of replacing feeder fixed-route transit routes in a suburban area by flex-route service. Three routes in the City of Oakville (a suburb of Toronto) that connect with the regional commuter rail (GO Transit) were chosen for the analysis. A simulation of the routes under the existing structure and under several flex-route designs was performed. The results show that assigning an appropriate slack time is essential in having an effective flex-route service and that more slack time does not necessarily translate into more ridership. The results also show that improving the performance of the transit service, in terms of passenger per vehicle revenue hours, will occur only if the ratio of the increase in ridership relative to the added slack time is more than the ratio of the existing ridership relative to the scheduled running time of the original fixed-route service.

Increasing urbanisation around the world has raised the passenger demand for public transport. Many public transport technologies are available to meet this demand. This paper develops a spreadsheet cost model that simulates public transport modes operated on a 12-km route and incorporates a new public transport mode, the straddle bus. Previous research has assumed that demand is exogenous (externally fixed). This work investigates the impact of endogenous demand, which varies with service level characteristics. Elasticities for passenger waiting time and in-vehicle time are used to assess actual passenger reactions to the attractiveness for different public transport technologies at different levels of demand. The result of this work shows the lowest average social and operator cost public transport technologies for different ranges of demand. Based on the default values used in the spreadsheet model, smaller size vehicles (e.g. personal rapid transit and minibus) dominate the low passenger demand range due to their short waiting times. Single-decker and double-decker buses appear to be the best option for a demand of around 3000–60 000 passengers per day (ppd), followed by the straddle bus (from around 60 000 to 100 000 ppd). The great capacity but high capital costs of underground make it the most cost-effective technology when other technologies are experiencing high congestion, with demand levels higher than 100 000 ppd.

Due to the fluctuating travel demand in the suburban area operators are unable to provide high-quality feeder transit by the fixed-route transit. To solve this problem, the idea of using flex-route transit instead of fixed-route transit as feeder transit is presented to provide the higher travel convenience in suburban area. First, to ensure the service level, the rejection rate and the ratio of travel time to direct time are used to constrain the flex-route transit. Then, the average travel time of passengers is used to represent the service quality of public transportation, and the comparison model between two transits as feeder transit is established. Finally, community in Nanjing of Jiangsu Province is selected as a case study. The results show that, for Chunhua community, flex-route feeder transit can better serve residents' travel, as long as the expectation of service quality improvement is not too high, the travel demand is smaller than 30 passengers/h and the headway is smaller than 30 min.

A dynamic station strategy to improve the performance of flex-route transit services

The volume of mobility flows are increasing day by day and the condition of the number of people using private transport modes contribute to traffic congestion. With the limited capacity of the road, one of the alternatives solution to reduce congestion is to optimize the use of public transport. The purposes of this study are to determine the factors that influence user’s satisfaction on the quality of public bus transportation service and determine variables that became identifier on the dominant factor affecting user’s satisfaction. The study was conducted for the public bus transportation between districts in the province of Bali, which is among the eight regencies and one municipality, using a questionnaire as a data collection instrument. Service variables determinant of user’s satisfaction in this study, described in 25 questions, which were analyzed using factor analysis. The results showed there were six factors that explain the satisfaction of users of public transport in Bali, with a total diversity of data that can be parsed by 61.436%. These factors are: Safety and comfort, Responsiveness, Capacity, Tangible, Safety, Reliability. The dominant factor affecting public transport user satisfaction is the safety and comfort, with the most influential variable is feeling concerned about the personal safety of users when on the bus.

In this paper, we model and simulate special use cases of on-demand shared mobility services for the City of Ann Arbor, MI. We define shared mobility as any motor-vehicle-served transportation option between private vehicles and public transit, such as taxis, demand-responsive transit, and dynamic shuttles. Here, we present and evaluate a suite of four different service types that could potentially complement existing transportation services in Ann Arbor. A novel aspect of this study is that it tests scenarios that were developed in consultation with city planners looking for insights into real-world problems. This study used fleet simulation software to test four service configuration scenarios for a hypothetical on-demand shared mobility service: citywide shuttle, a corridor-based downtown shuttle, a park and ride shuttle, and a transit-complementary service. Three levels of demand were tested for each scenario: 3%, 9%, and 15% of all private vehicle trips in the city. Findings indicated that citywide on-demand shared mobility services struggled to achieve higher vehicle occupancies than private vehicles at approximately 1.4. Service configurations with aggregated trip density resulted in slightly improved occupancies, as found in downtown- and park and ride shuttle scenarios. More impactful was aggregating demand by moving from “many-to-many” routing as with citywide floating services to “many-to-one” routing as with downtown- or park and ride shuttle services, which increased vehicle occupancy from 1.4 to almost 2. Lastly, we also discuss the potential benefits of reduced congestion and parking needs.

Giving priority to public transport vehicles at traffic signals is one of the traffic management strategies deployed at emerging smart cities to increase the quality of service for public transit users. It is a key to breaking the vicious cycle of congestion that threatens to bring cities into gridlock. In that cycle, increasing private traffic makes public transport become slower, less reliable, and less attractive. This results in deteriorated transit speed and reliability and induces more people to leave public transit in favor of the private cars, which create more traffic congestion, generate emissions, and increase energy consumption. Prioritizing public transit would break the vicious cycle and make it a more attractive mode as traffic demand and urban networks grow. A traditional way of protecting public transit from congestion is to move it either underground or above ground, as in the form of a metro/subway or air rail or create a dedicated lane as in the form of bus lane or light rail transit (LRT). However, due to the enormous capital expense involved or the lack of right-of-way, these solutions are often limited to few travel corridors or where money is not an issue. An alternative to prioritizing space to transit is to prioritize transit through time in the form of Transit Signal Priority (TSP). Noteworthy, transit and specifically bus schedules are known to be unstable and can be thrown off their schedule with even small changes in traffic or dwell time. At the same time, transit service reliability is an important factor for passengers and transit agencies. Less variability in transit travel time will need less slack or layover time. Thus, transit schedulers are interested in reducing transit travel time and its variability. One way to reach this goal is through an active intervention like TSP. In this chapter a comprehensive review of transit signal priority models is presented. The studies are classified into different categories which are: signal priority and different control systems, passive versus active priority, predictive transit signal priority, priority with connected vehicles, multi-modal signal priority models, and other practical considerations.

Sensitivity analysis and multi-criteria optimization of SMA cable restrainers for longitudinal seismic protection of isolated simply supported highway bridges

Majalengka Regency is a Level II area experiencing rapid traffic growth. Impact analysis of public transportation on traffic congestion is needed to maintain the smooth running of existing traffic roads in Majalengka, especially in the Simpeureum area on Jalan Pangeran Muhammad, which is the main route with several schools, including SMPN 1 Cigasong, being one of the areas where traffic congestion occurs in West Java Province. This paper aims to impact analysis of public transportation on traffic congestion and determine what effects of public transportation can cause traffic congestion. Often, drivers of public transportation or for example public transportation that park their cars carelessly when dropping off passengers on the side of the road and do not see the surroundings, blocking other vehicles that want to pass behind them so that traffic jams occur even if only a fraction of a second. This problem has long been a core issue in Indonesia, especially in areas with high public transportation use. As well as analyze related issues in the discussion. The research method was carried out by survey for the research source located in Simpeureum, which is on Jalan Pangeran Muhammad, which will go to Rajagaluh - Cirebon road in January 2025 and for data analysis using quantitative. The results showed that the impact of public transportation on traffic congestion still affects traffic roads by parking their vehicles carelessly when dropping off passengers so that other drivers have to wait. However, the relatively short time significantly affects other drivers' time. This paper concludes that congestion caused by public transportation also significantly impacts other drivers. This usually happens because the high rate of indiscriminate parking when dropping off passengers is due to the lack of supporting facilities such as bus or public transportation stops.

The paper studies a general bidirectional public transport line along which demand varies by line section. The length of line sections also varies, and therefore their contribution to aggregate (line-level) user and operational costs might be different, even if demand levels were uniform. The paper proposes the Gini index as a measure of demand imbalances in public transport. We run a series of numerical simulations with randomised demand patterns, and derive the socially optimal fare, frequency and vehicle size variables in each case. We show that the Gini coefficient is a surprisingly good predictor of all three attributes of optimal supply. These results remain robust with inelastic as well as elastic demand, at various levels of aggregate demand intensity. In addition, we find that lines facing severe demand imbalances generate higher operational cost and require more public subsidies under socially optimal supply, controlling for the scale of operations. The results shed light on the bias introduced by the assumption of homogeneous demand in several existing public transport models.