Bus Passengers Research Articles

Road testing of lateral acceleration in urban bus

Lateral acceleration is one of the parameters used to assess a driver’s behaviour and the comfort of urban bus passengers. Because of its purpose, a city bus is a vehicle designed to transport large numbers of people over relatively short distances. The specific driving conditions require constant stopping and accelerating. Lateral acceleration values collected in selected road manoeuvre tests or performance tests conducted in real traffic conditions will determine the lateral vehicle movement dynamics. This paper presents the results of lateral acceleration in a urban bus, obtained during experimental studies in selected road manoeuvres on various surfaces. The surface condition and type do not substantially affect the lateral acceleration when driving on a circular track. During test drives on a circular track, the range of lateral acceleration amounts to 5.56÷7.37 m/s2. The lateral acceleration obtained during double lane change attempts amounted to 2.84÷4.42 m/s2. The maximum lateral acceleration values achieved by the urban bus on a dry and wet asphalt surface are similar.

Experimental testing of longitudinal acceleration in urban buses

A vehicle’s longitudinal acceleration is one the parameters that characterize a vehicle’s motion dynamics and used, among others, to assess the comfort of public bus passengers. Rapid acceleration and braking performed by urban bus drivers can be deemed by passengers as uncomfortable and dangerous in some situations, especially for standing passengers. This paper presents an analysis of a public bus’s longitudinal acceleration recorded during extreme acceleration and braking attempts. The experimental tests were conducted on dry and wet asphalt and concrete surfaces. The test results allow for the statement that lateral acceleration depends largely on the road’s surface and its type. The maximum longitudinal acceleration values during rapid acceleration, depending on the surface, are within the narrow range of 2.18-2.81 m/s2. During braking, a public bus’s minimum longitudinal acceleration was within the range of -5.58÷-8.54 m/s2.

The Effect of Schedule, Rate and Passenger Complaints on Consumer Decisions Using the Bus Fleet of PT. ALS (Introduction to Sumatra)

This study aims to analyze the effect of schedules, fares and passenger complaints on consumer decisions to use the PT. ALS. The method used in this research is quantitative. The population in this study is ALS bus passengers with a total of 11,655 people using the ALS bus fleet for the last 3 months starting from December 2021 - February 2022. In this study, samples were taken using the Slovin formula, and using a probability sampling technique, namely proportionate stratified random sampling. The total number of respondents in this study were 101 consumers. The analytical method used is multiple linear regression method using SPSS Version 25.00 program. The results obtained from the coefficient value of 0.293, where the value of t-count > t-table (3.637 > 1.6602) and a significant value of 0.000 is smaller than 0.05, meaning that the schedule has a significant effect on consumer decisions, then Ha1 is accepted. The calculated F value is 70.779. With = 5%, dk of numerator: 4, dk of denominator: 101-3-1 (5%; 2; 97; Ftable 2.47) obtained F table value of 2.47. From this description it can be seen that F arithmetic (70,779) > F table (3,15), and a significance value of 0.000 <0.05, it can be concluded that the third hypothesis is accepted, meaning that the Schedule Variable (X1 ) , Tariff ( X2 ) and Passenger Complaints (X 3 ) have a simultaneous (simultaneous) effect on the Consumer Decision Variable (Y).

MEASURING THE EFFECTS OF PASSENGERS’ SOCIO-DEMOGRAPHIC FACTORS ON SATISFACTION WITH PUBLIC BUS SERVICES IN A DEVELOPING CITY: A CASE STUDY IN SEMARANG, INDONESIA

The present study aimed to assess passenger satisfaction with bus transit services based on passengers’ socio-demographic characteristics, given the service quality. An ordinal logistic regression analysis was conducted to relate passengers’ sociodemographic characteristics to their satisfaction with public bus services. The sociodemographic characteristics studied were age, gender, marital status, occupation, income, housing type, family size, and motor vehicle ownership. Data were collected by administering an onboard survey to public bus passengers. In total, 580 completed a questionnaire asking about their socio-demographic characteristics and their satisfaction with bus transit services. The study reveals that significant differences exist in the levels of passengers’ satisfaction depending on their socio-demographic characteristics. Greater satisfaction was significantly associated with being married, unemployed, and young. The delivery of public bus services needs to consider different segments of passengers.

A Complete Information Interaction-Based Bus Passenger Flow Control Model for Epidemic Spread Prevention

Because the strategy of stopping bus lines during an epidemic can negatively impact residents, this study proposes a bus passenger flow control model to optimize the safety of and access to bus transport. The information interaction environment can provide a means for the two-way regulation of buses and passengers. In this model, passengers first request their pick-up and drop-off location, and then the bus feeds back information on whether it accepts the request. Through this method, passenger flow control can be realized through complete information interaction. The study aimed to establish a multi-objective function that minimizes the weighted total cost of the safety cost, the passenger travel cost, and the bus travel cost during an epidemic. The constraints were the full load and riding rates of urban buses in peak periods under the condition of epidemic prevention and control. The results showed that, in the morning peak period, the passenger flow control scheme reduced the passenger infection probability by 17.89%, compared with no passenger flow control scheme. The weighted total cost of the epidemic safety cost, the passenger travel cost, and the bus operation cost was reduced by 8.04%. The optimization effect of the passenger flow control scheme of this model is good, and not only reduces the probability of passengers being infected, but also meets the requirements of epidemic prevention and the travel needs of residents.

Observational study on multi-type conflicts between passengers and cyclists at the bus stop – A case study in Nanjing

Dedicated cycleway is usually used to keep cyclists away from motorised vehicles in some countries like China. As a result, potential traffic conflicts between bus passengers and cyclists are frequently observed, which lead to potential safety issues for both passengers and cyclists. To our best knowledge, such conflicts and potential safety risk at the bus stop have received rare investigation. More specifically, multi-type conflicts caused by passengers converging from different directions with heterogeneous route choices lack proper research. To fill the gap, this study aims to investigate interactions and conflicts between bus passengers and cyclists. Unobtrusive observations and data collection are conducted by video recordings at a typical bus stop in Nanjing CBD, China. Trajectories of passengers and cyclists are extracted to analyse their interactions, conflicts, and potential safety risks. Lag-time and time-to-collision are adopted as indicators to evaluate the cycleway congestion and severity degree of conflicts, respectively. Multiple influential factors are analysed to evaluate their impacts to congestion and safety risk at a bus stop. The analysis results show that high density of passengers and cyclists, the behaviour of passengers walking along the right side of cycleway, and the interaction between passengers and cyclists crossing from behind or side will increase safety risk. The research findings suggest that bus stop layout design should consider clear view for crossing passengers and passing cyclists; sufficient space should be provided for waiting passengers; and crosswalk should be designed properly to guide passengers to cross cycleway and protect cyclists’ and passengers’ safety.

Exploring the effect of urban spatial development pattern on carbon dioxide emissions in China: A socioeconomic density distribution approach based on remotely sensed nighttime light data

Exploring the effect of urban spatial development pattern (UPD) on carbon dioxide emissions (CDEs) (EUC) is important for understanding low-carbon sustainable development. Numerous studies on EUC have mainly focused on individual cities or regions within the mixed conclusions due to the lack of reliable UPD indices and reasonable methods. Thus, taking China's 257 prefecture-level cities as experimental objects, a novel system approach was developed from the perspective of socioeconomic density distribution (SED) index to measure UPD on the basis of the Suomi National Polar-orbiting Partnership (NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light data. EUC was then analyzed on the basis of the dynamic panel data model from multiple perspectives. Results show that the SED index can effectively measure UPD with rich spatial information from multiple dimensions. The coefficients of SED and (SED)2 are 0.129 and − 1.240, respectively, indicating that EUC shows a clear inverted U-shaped curve in China, i.e., an increase in UPD compactness increases CDEs at the beginning, and when a certain height is reached, an increase in UPD compactness decreases CDEs. Heterogeneity analysis indicates a U-shaped curve of EUC is found in megalopolis, and inverse U-shaped curve are observed in medium and small cities. Bus passenger volume, energy consumption, infrastructure, and housing demand are proven as the transmission factors of EUC. It is suggested that utilizing the positive externality effect of agglomeration and accelerating the inflection point of the inverse U-shaped curve may be necessary because the improvement of urban socioeconomic agglomeration will improve the UPD compactness and reduce CDEs.

ESTIMATION OF INFLUENCE OF PSYCHOPHYSIOLOGICAL CONDITION OF THE DRIVER ON SAFETY OF PASSENGER AUTOMOBILE TRANSPORTATIONS

The safety of road passenger transport depends on the main key elements that make up the system: driver-car-road-environment. Failure of any element of this system increases the probability of its exit from the normal-functional state, and most importantly, will increase the risk of an accident (hereinafter - road accident). Therefore, the search for ways to reduce the likelihood of accidents during passenger road transport based on the analysis of the consequences of changes in the psychophysiological states of the driver is an urgent task.
 The purpose of the study is to develop recommendations for reducing the probability of accidents during passenger road transportation based on the analysis of the consequences of changes in the psychophysiological states of a driver. The study used the method of expert assessments and the method of "Failure Mode and Effects Analysis" (hereinafter the method "FMEA"), which includes organizational, logical and mathematical and statistical procedures aimed at obtaining from experts the assessment of hazards affecting the change the psychophysiological state of a driver while driving a passenger bus, their analysis and generalization of the results in order to prepare rational decisions.
 It is determined that changes in the psychophysiological state of a driver lead to errors in driving a passenger bus and are the main catalyst, which together with various hazards when driving a vehicle and joint interaction of the subject with the object - leads to traffic violations and further - to road accidents.
 It is shown that in case of elimination of one of the listed components, the road accident will not occur. It is established that the loss of concentration, changes in emotional state and intensity of the load are the main factors that affect the psychophysiological state of a passenger bus driver. The dependence of the accident on the presence of the catalyst, the hazard, and the interaction of the subject with it is determined. It is proposed to use the driver's reaction as a catalyst, which depends on the change of their psychophysiological state while driving a passenger bus.
 Keywords: driver, psychophysiological condition, passenger bus, hazard, interaction, catalyst.

Prediction of Bus Passenger Traffic using Gaussian Process Regression.

The paper summarizes the design and implementation of a passenger traffic prediction model, based on Gaussian Process Regression (GPR). Passenger traffic analysis is the present day requirement for proper bus scheduling and traffic management to improve the efficiency and passenger comfort. Bayesian analysis uses statistical modelling to recursively estimate new data from existing data. GPR is a fully Bayesian process model, which is developed using PyMC3 with Theano as backend. The passenger data is modelled as a Poisson process so that the prior for designing the GP regression model is a Gamma distributed function. It is observed that the proposed GP based regression method outperforms the existing methods like Student-t process model and Kernel Ridge Regression (KRR) process.

Impact analysis of actual traveling performance on bus passenger’s perception and satisfaction

Customer satisfaction has always been a pivotal indicator of public transport service quality. However, most existing studies on public transport satisfaction focused on passenger perception without considering the actual traveling performance. Thus, whether passenger perception veritably reflects the actual performance remains to be investigated. This paper adopts the gradient boosting decision trees approach to reveal the relationship between actual on-board performance and passenger satisfaction driven by individual perception. The work utilizes a mixed dataset combining customer satisfaction survey (CSS) with actual traveling data collected in Shijiazhuang, China. Instead of a direct equivalence, the results indicate the complex non-linear relationship between passenger perception and actual on-board performance. These findings enable deriving strategies for administrators and operators to improve the public transport service quality and customer satisfaction in a more effective and targeted way.

Designing Dynamic Inductive Charging Infrastructures for Airport Aprons with Multiple Vehicle Types

In the effort to combat climate change, the CO2 emissions of the aviation sector must be reduced. The traffic caused by numerous types of ground vehicles on airport aprons currently contributes to those emissions as the vehicles typically operate with combustion engines, which is why an electrification of those vehicles has already begun. While stationary conductive charging of the vehicles is the current standard technology, dynamic wireless charging might be an attractive technological alternative, in particular for airport aprons; however, designing a charging network for an airport apron is a challenging task with important technical and economic aspects. In this paper, we propose a model to characterize the problem, especially for cases of multiple types of vehicles sharing the same charging network, such as passenger buses and baggage vehicles. In a numerical study inspired by real-world airports, we design such charging networks subject to service level constraints and evaluate the resulting structures via a discrete-event simulation, and thus, show the way to assess the margin of safety with respect to the vehicle batteries’ state of charge that is induced by the spatial structure of the charging network.

BUS STOPS IN BELÉM-PARÁ

Introduction: This work has as theme the passenger shelters in Belém, Pará, in the year 2022. Objective: to analyze the conditions of the shelters for bus passengers, based on the assumption of the need to wait for the arrival of the vehicle in a place with accessibility and adequate physical conditions. Methodological procedures: descriptive research, qualitative approach, using the bibliographic and field research, mediated by observation, from images of bus stops, located in Belém, capital of Pará, in 2022, chosen randomly, aiming for greater representativeness of the object under study. Results: It can be inferred that there are not enough shelters to meet the demand since, of the total of 1,513 bus stops, only 570 (38%) have passenger shelters; the conservation conditions are extremely poor, with a lack of coverage and side protection, seats and spatial orientation information. Conclusion: Observing the substantial absence of shelters for bus passengers in Belém, it is suggested that the government, together with the private sector, seek alternatives for implementing such urban equipment, considering the demand for buses in Belém, as well as the climatic characteristics of the city.

Trip-pair based clustering model for urban mobility of bus passengers in Macao

ABSTRACT Public transit is a major mode of urban mobility in Macao, a compact and populous city in China. Public transit ridership is related to round-the-clock shift-based work arrangements in the gaming industry. Smartcards that are used in transit fare collection are a vital data source for understanding the travel patterns and characteristics of transit passengers. However, transactional information from single data sources is fragmented, hindering the extraction of mobility patterns. We proposed a trip-pair-based clustering model that extracts the within-day mobility patterns of bus riders. DBSCAN is employed to filter scattered trips and hierarchical agglomerative clustering to identify clusters. Using a six-month smartcard dataset from Macao, categories of bus passengers were identified. Travel patterns exist with irregular, one-way, and round-trip passengers, and shift workers. The unique shift-based work-life and mixed travel modes in Macao can serve as an example of dense and compact city travel for smart cities.

Understanding the perceived behavior of public utility bus passengers during the era of COVID-19 pandemic in the Philippines: Application of social exchange theory and theory of planned behavior

Over the years, passengers in the Philippines have relied on public utility bus (PUB) to reach a destination. However, PUB has been greatly affected by the adverse effects of the coronavirus disease (COVID-19) outbreak. Therefore, the study analyzed passengers' behavior using PUB during the COVID-19 pandemic by integrating social exchange theory (SET) and the theory of planned behavior (TPB). A total of 505 PUB users completed the online questionnaire containing sixty-seven (67) measures. Through confirmatory factor analysis – structural equation modeling (CFA-SEM), all the twelve (12) hypotheses were found significant. Accessibility, safety, economic benefit, and crisis management had a significant and positive influence on passengers' trust. Moreover, passengers' trust produced a significant positive influence on attitude, subjective norm, and perceived behavioral control. Additionally, attitude, subjective norm, and perceived behavioral control generated a significant positive influence on the intention to use PUB. Furthermore, perceived behavioral control had a significant positive influence on perceived passenger behavior. Lastly, the intention to use PUB developed a substantial and positive influence on perceived passenger behavior. PUB companies and policymakers can utilize these findings to develop additional PUB reforms for the benefit of passengers, drivers, operators, companies, and the government during the COVID-19 pandemic.

AI-based neural network models for bus passenger demand forecasting using smart card data

Accurate short-term forecasting of public transport demand is essential for the operation of on-demand public transport. Knowing where and when future demands for travel are expected allows operators to adjust timetables quickly, which helps improve service quality and reliability and attract more passengers to public transport. This study addresses this need by developing AI-based deep learning models for prediction of bus passenger demands based on actual patronage data obtained from the smart-card ticketing system in Melbourne. The models, which consider the temporal characteristics of travel demand for some of the heaviest bus routes in Melbourne, were developed using real-world data from 18 bus routes and 1,781 bus stops. LSTM and BiLSTM deep learning models were evaluated and compared with five conventional deep learning models using the same data set. A desktop comparison was also undertaken against a number of established demand forecasting models that have been reported in the literature over the past decade. The comparative evaluation results showed that BiLSTM models outperformed other models tested and was able to predict passenger demands with over 90% accuracy.

Bridging strategy for the disruption of metro considering the reliability of transportation system: Metro and conventional bus network

With the frequent occurrence of metro disruption, how to guarantee the reliability of the system has gradually become a research hotspot. Previous studies mostly used the bridging bus to maintain the service level, and ignored the experience of passengers, the reliability of conventional bus system, the heterogeneity of passenger and underutilized the capacity. Hence, to deal with the deficiencies, a comprehensive bridging strategy is proposed to balance the benefit of stranded metro passengers and conventional bus passengers considering the dynamic changes in passenger demand. We develop a tailor-made integration framework for operators, dispatch bridging buses from multiple alternative sources, and design two operated access methods: Station-Station docking method and demand-responsive method to satisfy the heterogeneity of passenger. A bi-level programming model is established to describe the strategy and to determine operation scheme. And then, a two-layer multi-objective genetic algorithm is used to solve the bi-level model. A case study is conducted based on the real case of Guangzhou Metro disruption in 2019. The superiority of the proposed strategy over the two benchmark strategies is demonstrated by the results. Notably, the study provides some suitable and operable evacuation strategy for transit operators to guarantee residents’ daily travel plan with the sensitivity analysis.

An Intelligent Time-Series Model for Forecasting Bus Passengers Based on Smartcard Data

Public transportation systems are an effective way to reduce traffic congestion, air pollution, and energy consumption. Today, smartcard technology is used to shorten the time spent boarding/exiting buses and other types of public transportation; however, this does not alleviate all traffic congestion problems. Accurate forecasting of passenger flow can prevent serious bus congestion and improve the service quality of the transportation system. To the best of the current authors’ knowledge, fewer studies have used smartcard data to forecast bus passenger flow than on other types of public transportation, and few studies have used time-series lag periods as forecast variables. Therefore, this study used smartcard data from the bus system to identify important variables that affect passenger flow. These data were combined with other influential variables to establish an integrated-weight time-series forecast model. For different time data, we applied four intelligent forecast methods and different lag periods to analyze the forecasting ability of different daily data series. To enhance the forecast ability, we used the forecast data from the top three of the 80 combined forecast models and adapted their weights to improve the forecast results. After experiments and comparisons, the results show that the proposed model can improve passenger flow forecasting based on three bus routes with three different series of time data in terms of root-mean-square error (RMSE) and mean absolute percentage error (MAPE). In addition, the lag period was found to significantly affect the forecast results, and our results show that the proposed model is more effective than other individual intelligent forecast models.

Assessment of the satisfaction of Bus passengers in Dhaka, Bangladesh

Increased population results in an increase in travel demand. Greater road length and construction of new roads result in faster and longer travels and increase car ownership, which contributes to increased traffic congestion and pollution. The bus form of travel is a critical component of resolving this issue. A bus can carry 30-40 passenger per trip on the other hand a private car carries 2-3 passengers per trip. So, bus transport could decrease the number of vehicles in road if it can provide better service quality in bus mode. In this experiment, the main objectives are to better understand overall passengers’ satisfaction in bus transport in Uttara-Gazipur route and predict the individual factors contributing to the comfort of the bus modes in this route. To evaluate this result, a questionnaire survey form was created with about 19 service quality parameters such as safe speed, temperature, noise, required time, waiting time for bus, station service, seat comfort service, cleanness, staff behaviour, payment system fare. In this experiment, opinions of 100 passengers were collected regarding the service quality. Data were analyzed using descriptive and multiple linear regression, analyzed by SPSS software. One main finding reported that passengers are not satisfied yet with bus service quality. This study revealed that 75% of feedback was poor or very poor, and 25% of feedback was positive or good regarding the level of satisfaction for bus mode. The regression analysis reported that the overall P-value is less than 0.05 and the regression coefficient, R2 is 0.64, which rejected the null hypothesis and makes the survey significant to assess the overall comfort of passengers traveling with bus in Uttara-Gazipur route. It was also found that three parameters: reliability (ẞ = 0.170, P=0.027), safe speed (ẞ = 0.240, P = 0.021) and travel time (ẞ = 0.320, P = 0.01) are individually contributing to overall satisfaction of passenger in Uttara-Gazipur route.

Ergonomic Design of Apron Bus with Consideration for Passengers with Mobility Constraints

Passengers in an apron bus are usually subjected to a standing position because of its limited seats and capacity. Due to this, passengers, especially those with mobility constraints, may expose themselves to musculoskeletal disorder (MSD) risks such as body pain, discomfort, and non-collision injuries. The purpose of this study is to design an ergonomic apron bus to aid the musculoskeletal discomfort experienced by passengers with mobility constraints, specifically the elderly, pregnant women, mothers carrying infants, and persons needing wheelchair assistance. A total of 149 participants are involved in the study. Corlett’s and Bishop’s body discomfort questionnaires and Rapid Entire Body Assessment (REBA) are utilized to evaluate the respondent’s experience of discomfort in different regions of their body. The results show that passengers with mobility constraints experience body discomfort during the apron bus ride. The prevalence of body discomfort is evident in the lower back, knee, thigh, arm, shoulder, and middle back. Finally, principles of anthropometry are used in the study along with quality function deployment (QFD), failure mode and effects analysis (FMEA), and cost-benefit analysis to evaluate the feasibility of the recommended ergonomic design of the apron bus. To meet the requirements of people with disabilities, the ergonomic design of an apron bus is created to minimize the risk of exposure of passengers to certain musculoskeletal discomfort, maximize the space, minimize the delay time of the airlines, and be able to prioritize passengers who require mobility assistance.

Spatiotemporal Hashing Multigraph Convolutional Network for Service-Level Passenger Flow Forecasting in Bus Transit Systems

Multistep service-level passenger flow forecasting is of great value in bus transit systems. This task is faced with great challenges due to complicated and dynamic spatial–temporal dependencies, such as interstation semantic dependencies, interline spatial dependencies, and interservice temporal dependencies, which are not effectively modeled by existing methods. To address these challenges, we propose a spatiotemporal hashing multigraph convolution network, called ST-HMGCN. ST-HMGCN constructs two types of subgraphs from perspectives of physical adjacency and semantic similarity to explicitly capture spatial–temporal dependencies among bus stations/lines, and integrates the interservice temporal correlations to achieve the service-level bus passenger flow forecasting. Moreover, it utilizes the hashing graph convolution to extract the dynamic spatial correlations among graph nodes. Furthermore, a temporal-attention block with residual connections is used to model the nonlinear temporal correlations between different time intervals of each station, which significantly reduces the error propagation among prediction time steps. Finally, we use a large-scale real bus operation data set to conduct an extensive evaluation of ST-HMGCN and 11 state-of-the-art baselines, and further leverage the passenger prediction results of our model to provide crowdedness-aware route recommendation. The experimental results verify the effectiveness of the proposed modeling method and its application value in intelligent transportation.