Enhancing service quality of rural public transport during the COVID-19 pandemic: a novel fuzzy approach

In order to encourage the use of public transportation, it is necessary to make it more appealing to commuters by conducting frequent Service Quality (SQ) evaluations and modifications. Understanding passengers' expectations of public transportation are important, and evaluating the SQ is an essential tool for assessing the overall performance of the public transportation system. The purpose of the present study was to examine the expectations and perceptions of core passengers regarding SQ in public bus transportation. By surveying 598 passengers in rural public transportation in India, the study results are illustrated and further discussed to guide possible bus SQ improvements in rural areas. In addition, the impact of these expectations and perceptions on satisfaction levels of rural public bus transportation services are explored by applying the Interval-Valued Pythagorean Fuzzy (IVPF). The outcomes of the survey indicated significant disparities among expectations and perceptions of passengers, as well as widespread dissatisfaction with the delivery of bus services in rural areas as a whole. The dependability and adaptiveness of the bus service have been critical in describing the overall quality of bus services in rural areas, and best practices from around the world were used to develop a set of recommendations for transportation operators and local officials. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Origin Destination Estimation Carrying over Rolling Proximity Identifiers with RSSI

Origin destination (OD) data describing passengers' flows is essential for improving bus route operational efficiency. Manual collection of OD data is still conducted, so automatic OD data acquisition using the internet of things (IoT) is desired. One method utilizes Bluetooth beacon identifiers to understand passengers' flows while considering their privacy. Still, while random MAC addresses can estimate the number of devices there, they are insufficient for generating ODs. In contrast, in response to the COVID-19 pandemic, the government promoted the exposure notification system to prevent secondary infection. The smartphone app exchanges short-term identifiers called Rolling Proximity Identifiers (RPIs), updated every 15 minutes. This research aims to realize tracking during bus rides with only a few RPIs carryovers, since bus rides are only about an hour long at most. We evaluated the system on a bus in Kyoto City and successfully tracked passengers for 55 minutes, the experiment's maximum length. © 2022 IEEE.

Bus Network Design and Frequency Setting in the Post-COVID-19 Pandemic: The Case of London

A transit network design frequency setting model is proposed to cope with the postpandemic passenger demand. The multiobjective transit network design and frequency setting problem (TNDFSP) seeks to find optimal routes and their associated frequencies to operate public transport services in an urban area. The objective is to redesign the public transport network to minimize passenger costs without incurring massive changes to its former composition. The proposed TNDFSP model includes a route generation algorithm (RGA) that generates newlines in addition to the existing lines to serve the most demanding trips, and passenger assignment (PA) and frequency setting (FS) mixed-integer programming models that distribute the demand through the modified bus network and set the optimal number of buses for each line. Computational experiments were conducted on a test network and the network comprising the Royal Borough of Kensington and Chelsea in London. © 2023 American Society of Civil Engineers.

Combined Effect of Changes in Transit Service and Changes in Occupancy on Per-Passenger Energy Consumption

Many transit providers changed their schedules and route configurations during the COVID-19 pandemic, providing more frequent bus service on major routes and curtailing other routes, to reduce the risk of COVID-19 exposure. This research first assessed the changes in Metropolitan Atlanta Rapid Transit Authority (MARTA) service configurations by reviewing the prepandemic versus during-pandemic General Transit Feed Specification (GTFS) files. Energy use per route for a typical week was calculated for pre-pandemic, during-closure, and post-closure periods by integrating GTFS data with MOVES-Matrix transit energy and emission rates (MOVES signifying MOtor Vehicle Emission Simulator). MARTA automated passenger counter data were appended to the routes, and energy use per passenger-mile was compared across routes for the three periods. The results showed that the coupled effect of transit frequency shift and ridership decrease from 2019 to 2020 increased route-level energy use for over 87% of the routes and per-passenger-mile energy use for over 98% of the routes. In 2021, although MARTA service had largely returned to pre-pandemic conditions, ridership remained in an early stage of recovery. Total energy use decreased to about pre-pandemic levels, but per-passenger energy use remained higher for more than 91% of routes. The results confirm that while total energy use is more closely associated with trip schedules and routes, perpassenger energy use depends on both trip service and ridership. The results also indicate a need for data-based transit planning, to help avoid inefficiency associated with over-provision of service or inadequate social distancing protection caused by under-provision of service. © National Academy of Sciences: Transportation Research Board 2022.

Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review

Management of crowd information in public transportation (PT) systems is crucial, both to foster sustainable mobility, by increasing the user's comfort and satisfaction during normal operation, as well as to cope with emergency situations, such as pandemic crises, as recently experienced with coronavirus disease (COVID-19) limitations. This article presents a taxonomy and review of sensing technologies based on the Internet of Things (IoT) for real-time crowd analysis, which can be adopted in the different segments of the PT system (buses/trams/trains, railway/metro stations, and bus/tram stops). To discuss such technologies in a clear systematic perspective, we introduce a reference architecture for crowd management, which employs modern information and communication technologies (ICTs) in order to: 1) monitor and predict crowding events;2) implement crowd-aware policies for real-time and adaptive operation control in intelligent transportation systems (ITSs);and 3) inform in real time the users of the crowding status of the PT system, by means of electronic displays installed inside vehicles or at bus/tram stops/stations and/or by mobile transport applications. It is envisioned that the innovative crowd management functionalities enabled by ICT/IoT sensing technologies can be incrementally implemented as an add-on to state-of-the-art ITS platforms, which are already in use by major PT companies operating in urban areas. Moreover, it is argued that, in this new framework, additional services can be delivered to the passengers, such as online ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning. © 2001-2012 IEEE.

Flexible Scheduling Model of Bus Services between Venues of the Beijing Winter Olympic Games

Traditional buses travel on fixed routes and areas, which cannot satisfy the flexible demands of athletes in the context of COVID-19 and the closed-loop traffic management policy during the 2022 Beijing Winter Olympic Games (BWOG). This study predicts the travel demands based on the characteristics of athletes' daily travel demands and then presents a flexible bus service scheduling model for cross-region scheduling among Beijing, Yanqing, and Zhangjiakou to provide high-level service. The flexible bus service is point-to-point and avoids unnecessary contact, which reduces the risk of spreading COVID-19 and ensures athletes' safety. In this study, the flexible bus scheduling model is established to optimize scheduling schemes, whose object is to minimize the cost of the system based on some realistic constraints. These constraints consider not only the preferred time windows of athletes' demand but also the vehicle's capacity, depot, minimum load factor, total demands, etc. In addition, a genetic-simulated annealing hybrid algorithm (GSAHA) is designed to solve the model based on the characteristics of the genetic algorithm (GA) and simulated annealing. To assess the feasibility and efficiency of the model and algorithm, a case study is conducted in the Beijing-Yanqing area. Furthermore, the travel time of the flexible bus is compared to that of the traditional bus, according to the results of the case study. Moreover, the sensitivity of the model and algorithm are analyzed. The experimental results show that the proposed model and algorithm can dispatch buses with superior flexibility and high-level services during the BWOG. [ FROM AUTHOR]

Research on the characteristics of regular bus trips in Guangzhou under the influence of the new crown epidemic based on multivariate data fusion

Conventional public transportation is an important part of public transportation, and it has always been the focus of urban transportation research to excavate the characteristics of public transportation and analyze residents' travel patterns. In 2020, the new crown epidemic broke out. The outbreak and continuation of the epidemic have caused shocks and challenges to conventional public transportation, and the characteristics of conventional public transportation have developed significantly. Taking Guangzhou as an example, this paper conducts bus IC card mining based on multi-source data fusion, and conducts research on the characteristics of changes in Guangzhou's regular bus travel rules under the influence of the new crown epidemic. Research shows that under the continuous influence of the epidemic, the scale of bus trips has dropped significantly, the attraction of conventional buses to commuter passengers has been weakened, special groups are important users of public transport, and the ride code has become the most important payment method. © 2022 SPIE.

Model predictive control of bus platoons in a circular route considering dispatching, holding and boarding limits

Public transport forms the backbone of the city's operation. Proper planning and investment of public transport can create additional jobs to revitalize and recover cities from covid-19. In this paper, we propose a combined dispatching-operation bus model predictive control strategy, where a rolling horizon mechanism is adopted to control the bus system in a real-time manner. Either a bus platoon or a single bus is allowed to be dispatched in each trip, and bus re-dispatching is captured in the system to realistically reflect the real-world. Also, the additional bus initial constraints allow control to be applied at any time when buses are either driving on the road or loading at the stop. Model complexity is investigated by solving the optimization problem under various prediction horizons, number of buses and bus stops. Furthermore, the comparison experiment with a high-frequency fixed dispatching method is performed on the Singapore bus line 179A developed in SUMO simulator to illustrate the effectiveness of the proposed method. © 2022 IEEE.

Based on the Design of Real-Time Monitoring of the Number of Bus Passengers Under the COVID-19 Situation

On the basis of convenient and practical design of a real-time monitoring bus station and arrival time, bus number real-time monitoring system. The system uses MCU and GPS as the controller, which can realize the statistics of the number of people entering and leaving the bus, fuse, process and analyze the data with bus data and bus stations and lines, and alarm when the human body temperature exceeds a certain range of management. Especially during the epidemic period, it provides a good safety environment for passengers, and also provides data basis for the optimization of intelligent bus. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The COVID-19 Pandemic and Public Transportation Usage in Utah

This study aims to investigate public transportation usage in the state of Utah during the period from 2017 to 2020. The study also aims to understand how the COVID-19 pandemic has affected the system in the year 2020. Based on the ridership data for different transportation modes including commuter rail, light rail, paratransit, and regular bus, the regular bus was the most used public transportation mode followed by light rail. The results also showed that the population is growing in Utah while the ridership for different modes is declining except for commuter rail. The system ridership decreased by almost half during the COVID-19 pandemic in 2020. The highest impacted mode was the commuter rail followed by the light rail. The lowest impacted mode was the regular bus. © 2022 IEEE.

Bus Passenger Load Prediction: Challenges from an Industrial Experience

In times of ongoing pandemic outbreak, public transportation systems organisation and operation have been significantly affected. Among others, the necessity to implement in-vehicle social distancing has fostered new requirements, such as the possibility to know in advance how many people will likely be on a public bus at a given stop. This is very relevant for both potential passengers waiting at a stop, and for decision makers of a transit company, willing to adapt the operational planning. Within the domain of data-driven Intelligent Transportation Systems (ITS), some research activities are being conducted towards Bus Passenger Load (BPL) predictions, with contrasting results. In this paper we report on an academic/industrial experience we conducted to predict BPL in a major Italian city, using real-world data. In particular, we describe the difficulties and challenges we had to face in the data processing and mining steps, due to multiple data sources, with noisy data. As a consequence, in this paper we highlight to the ITS community the need of more advanced techniques and approaches suitable to support the instantiation of a data analytic pipeline for BPL prediction. © 2022, Springer Nature Switzerland AG.

Optimizing operational strategies for mass transit systems in response to a global pandemic

This study analyzes the risk involved in riding various transit modes during and after a global pandemic. The goal is to identify which factors are related to this risk, how such a relationship can be represented in a manner amenable to analysis, and what a transit operator can do to mitigate the risk while running its service as efficiently as possible. The resulting infection risk model is sensitive to such factors as prevalence of infection, baseline transmission probability, social distance, and expected number of human contacts. Built on this model, we formulate, analyze and test three versions of a transit operator's design problem. In the first, the operator seeks to jointly optimize vehicle capacity and staff testing frequency while keeping the original service schedule and satisfying a predefined infection risk requirement. The second model assumes the operator is obligated to meet the returning demand after the peak of the pandemic. The third allows the operator to run more than one transit line and to allocate limited resources between the lines, subject to the penalty of unserved passengers. We find: (i) The optimal profit, as well as the testing frequency and the vehicle capacity, decreases when passengers expect to come in close contact with more fellow riders in a trip;(ii) Using a larger bus and/or reducing the testing cost enables the operator to both test drivers more frequently and allow more passengers in each bus;(iii) If passengers weigh the risk of riding bus relative to taxi, a higher prevalence of infection has a negative effect on transit operation, whereas a higher baseline transmission probability has a positive effect;(iv) The benefit of improving service capacity and/or testing more frequently is limited given the safety requirement. When the demand rises beyond the range of the capacity needed to maintain sufficient social distancing, the operator has no choice but to increase the service frequency;and (v) In the multi-line case, the lines that have a larger pre-pandemic demand, a higher penalty for each unserved passenger, or a greater exposure risk should be prioritized. © 2022 Elsevier Ltd

Bus Predictive-Control Method considering the Impact of Traffic Lights

Amid the COVID-19 pandemic, many travelers have switched from public transit to other modes. How to maintain the stability and service quality of the bus system under regular pandemic prevention and control, so as to maintain the attractiveness of the bus, is an important research direction. Predicting operation states and adopting appropriate control measures for running buses are effective means of improving the bus system's schedule reliability and service quality. Focusing on the impacts of intersection traffic lights on the link's travel time durations, we establish a probabilistic prediction model for bus headways, classifying the bus headways into three states: bunching, stable, and big gap states. Based on the prediction of bus headways, the most suitable control strategy is selected by the proposed method from the plan set, such as holding control, speed-adjusting control, and stop-skipping control to minimize the bus headway deviation. Simulation experiments were employed to verify the effectiveness of the proposed method. Compared with the no-control situation, the expected headway variation, average passenger waiting time, and bus bunching frequency for 100 simulations by the proposed method are reduced by 77.73%, 41.66%, and 87.11%, respectively. Compared with some control methods without prediction, the proposed method is more robust, maintains good control performance, and reduces bus bunching despite significant variations in environmental parameters. In addition, the model still performs well when considering the execution errors of bus drivers. [ FROM AUTHOR] Copyright of Journal of Advanced Transportation is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Android based advanced bus reservation system in the pandemic of Covid-19

Social distancing is one of the major requirements of Covid-19. India is a country with a huge population of middle and low financial income and they depend on public transportation to a large extent. In this context, to avoid social distancing while transportation, we propose, android based bus booking system. To achieve this purpose, we have used android studio, because it is an officially developed environment for Android OS. It is built on JetBrains' IntelliJ IDEA software that enables to provide a simple platform to users to check bus availability and reserve it with ease. The parameters like booking the seats between desired source and destination location, desired date, desired type of bus like AC or Non-AC, sleeper or sitting, reserving multiple tickets, and canceling them if required are considered. © 2022 IEEE. All rights reserved

The Day-Ahead Forecasting of the Passenger Occupancy in Public Transportation by Using Machine Learning

Public transport is one of the main infrastructures of a sustainable city. For this reason, there are many studies on public transportation which mostly answer the question of “when my next bus will arrive?”. However now when the public is under the restrictions of the Covid-19 pandemic and learning to live with new social rules such as “social distance” a new yet crucial question arise on public transportation: “how crowded my next bus will be?” To prevent the crowdedness in public transportation the traffic regulators need to forecast the number of passengers the day ahead. In this study, in cooperation with Synteda, we suggest a machine learning algorithm that forecasts the occupancy in a bus or tram the day ahead for each stop for a route. The input data is past passenger travel data provided by the Västtrafik AB which is the public transportation company in Gothenburg, Sweden. The hourly data for the precipitation and temperature also has been added to the forecasting method;the database of precipitation and temperature is obtained by the SMHI, Swedish Meteorological and Hydrological Institute. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

The Effect of COVID-19 on the Transit System in Two Regions: Japan and USA

The communication revolution that happened in the last ten years has increased the use of technology in the transportation world. Intelligent Transportation Systems wish to predict how many buses are needed in a transit system. With the pandemic effect that the world has faced since early 2020, it is essential to study the impact of the pandemic on the transit system. This paper proposes the leverage of Internet of Things (IoT) devices to predict the number of bus ridership before and during the pandemic. We compare the collected data from Kobe city, Hyogo, Japan, with data gathered from a college city in Virginia, USA. Our goal is to show the effect of the pandemic on ridership through the year 2020 in two different countries. The ultimate goal is to help transit system managers predict how many buses are needed if another pandemic hits. © 2021 IEEE.

Analyzing Rail Traffic Diversion Based on Machine Learning Technique considering Transportation Security

Based on a stated preference survey, we comprehensively analyze the travel psychology of residents and the advantages and disadvantages of rail transit and conventional buses, travel time, travel cost, travel security, and vehicle comfort and investigate the relationship between the relevant influencing factors and the transition probability from rail transit to buses. A stochastic utility theory is introduced to describe the transfer behavior pertaining to travel modes, and a binary Logit model for diversion transfer is constructed. The decision tree is also used to predict the diversion transfer. Then, based on the large amount of travel willingness data obtained through the stated preference survey, a maximum likelihood estimation method is used to calibrate the parameters of the binary Logit model. The performance of the binary Logit proves to be better than that of the decision tree. Results show that the travel time most notably affects the passenger flow transfer, followed by the vehicle comfort. Finally, Guangzhou Rail Transit Line 3 is considered an example, and the diversion route planning and design are performed according to the constructed diversion transfer probability model to verify the effectiveness and practicability of the model. The research provides an effective theoretical basis and technical reference for other cities to perform rail traffic diversion planning. Based on these results, the following suggestions can be made: (1) the organization of public transportation routes, delivery volume, and travel speed outside should be improved;(2) undertaking combined operation of bus and rail transportation and integrated development is preferred;(3) the transportation management should focus on the comprehensive function development and hardware support of public transportation stations. The convenience and comfort of rail transit are closely related to the facilities and functions of the stations and their connections, which should be highly valued.

Modern Technology for Evolving Mass Public Transportation in Cities

Buses and Rapid transit are an integral part of the mass transit system, especially in big cities have become a lifeline for many people. It has made commuting much easier and faster. With the increase in population, there has been a massive surge in the number of passengers leading to crowding at platforms and bus stops. Since the frequency of buses and rapid transit is uneven and lacks proper real-Time tracking of buses, we see a vast discrepancy in the number of passengers traveling. Such discrepancies not only have a vast economic impact but also make travelling by buses difficult for regular commuters, also increasing the travelling time. Especially considering the COVID 19 situation, it can cause a lot of problems. Thus, there is a need for a system that can adjust itself according to the number of passengers and real-Time tracking of public transportation systems available for passengers.With this paper, we aim towards providing an intelligent transportation system using real-Time data to manage the frequency of mass transit systems by crowdsourcing people on bus stands in real-Time using CCTV, analyzing the data, and making decisions realtime on the frequency of these mass transit systems by analyzing data through the help of data science and machine learning which would help in automation of rapid transit systems. © 2021 IEEE.

Research on the Effect of Public Transport Control Measures under the COVID-19 Epidemic

The outbreak of COVID-19 in 2020 greatly impacted China's transportation industry. This paper aims to analyze this impact and the indispensable role of public transport control measures in preventing the spread of the epidemic. The impact of SARS and COVID-19 was compared. Taking Hebei Province as an example, the impact of the epidemic on the public transport industry was analyzed. The information of Jincheng public transport network and bus IC card data were selected for analysis. The parameters of bus network density and bus line repetition coefficient were calculated. The number of bus departures of each line before and after Jincheng epidemic was counted. Based on the data of bus IC card, the dynamic network of passenger contact was constructed, and the passenger contact network diagram was drawn. The analysis results can inform and enable urban public transport departments to take control measures when public health emergencies occur. © 2021 CICTP 2021: Advanced Transportation, Enhanced Connection - Proceedings of the 21st COTA International Conference of Transportation Professionals. All rights reserved.