Bus Travel Time Research Articles

STUDY ON DYNAMIC INFLUENCE OF PASSENGER FLOW ON INTELLIGENT BUS TRAVEL SERVICE MODEL

To improve the service quality and convenience of bus travel services, this paper proposes the Intelligent Bus Travel Service Model (IBTSM). The IBTSM makes it possible to provide a travel strategy considering every aspect of bus travel, specifically, delay in the peak period arising from limited carrying-capacities of buses. A three-step approach was executed toward implementing the IBTSM. First, the bus travel-time was predicted using Long Short-Term Memory (LSTM). Next, the crowding level in the bus was evaluated using a fuzzy expert system, based on which a reasonable start-off time was planned, and the delay caused by large passenger flow was circumvented. The k-Nearest Neighbours (k-NN) algorithm was used to provide input data of passenger flow. In this study, the correlation between passenger flow variation and bus services was investigated to extend the provisions of the travel strategy to include start-off time scheduling and target bus selection, rather than only bus running-time estimation. The proposed model was evaluated using a bus in China as a case study, and its reliability and positive impact on promoting both the quality of bus services and development of intelligent travel were demonstrated.

Performance characterization of reinforcement learning-enabled evolutionary algorithms for integrated school bus routing and scheduling problem

Bi-objective school bus scheduling optimization problem that is a subset of vehicle fleet scheduling problem is focused in this paper. In the literature, school bus routing and scheduling problem is proven to be an NP-Hard problem. The processed data supplied by our framework is utilized to search a near-optimum schedule with the aid of reinforcement learning by evolutionary algorithms. They are named as reinforcement learning-enabled genetic algorithm (RL-enabled GA), reinforcement learning-enabled particle swarm optimization algorithm (RL-enabled PSO), and reinforcement learning-enabled ant colony optimization algorithm (RL-enabled ACO). In this paper, the performance characterization of reinforcement learning-enabled evolutionary algorithms for integrated school bus routing and scheduling problem is investigated. The efficiency of the conventional algorithms is improved, and the near-optimal schedule is achieved significantly in a shorter duration with the active guidance of the reinforcement learning algorithm. We attempt to carry out extensive performance evaluation and conducted experiments on a geospatial dataset comprising road networks, trip trajectories of buses, and the address of students. The conventional and reinforcement learning integrated algorithms are improving the travel time of buses and the students. More than 50% saving by the conventional and the reinforcement learning-enabled ant colony optimization algorithm compared to the constructive heuristic algorithm is achieved from 92nd and 54th iterations, respectively. Similarly, the saving by the conventional and the reinforcement learning-enabled genetic algorithm is 41.34% at 500th iterations and more than 50% improvement from 281st iterations, respectively. Lastly, more than 10% saving by the conventional and the reinforcement learning-enabled particle swarm algorithm is achieved from 432nd and 28th iterations, respectively.

Information entropy evaluation model of bus-line reliability considering the combination of bus stops and bus travel time

Evaluating the reliability of a bus line can provide useful information on the service performance of the city’s bus transit system and the need for improvements. This paper presents a reliability evaluation model of a bus line based on information entropy. The model integrates the weighted bus-stop index and the variation index of bus travel time. To better determine the weights of the bus stop, six indices were established based on three characteristics: passenger flow, bus-stop properties, and the environment. The proposed model was applied to typical bus lines in Fuzhou, People’s Republic of China. The results show that most bus-stop reliabilities at the peak period were smaller than those at the off-peak period. In addition, for either peak or off-peak period, the reliability shows a slight upward trend following a gradual decline after the first stop, generally reflecting the influence of passenger flow, environment, and location factors.

Role of built-environment, lifestyle, attitude and behavioral factors on mode choice- case of grocery shopping in Tehran

Although previous studies show that attitudes (personal preference) play an essential role in mode choice, few studies investigate the effect of heterogeneity of these factors in mode choice of grocery shopping. This research investigates the role of socioeconomic, built-environment, lifestyle, attitude, and behavioral variables on taste variation of individuals in mode choice of grocery shopping trips. 398 individuals are surveyed in chain grocery stores of Tehran in May 2016 and factor analysis is used to identify latent variable such as safety, pedestrian-oriented, mode flexibility. Furthermore, a mixed logit (MXL) model is employed to test heterogeneity among individuals. Results of MXL model show that several socioeconomic, mode attributes and attitude variables have significant effects on mode choice for grocery shopping activities. Besides, male variable in bus mode and travel time in walking mode is heterogeneous variables among parameters with random normal distribution coefficient for grocery shopping destinations. Moreover, the results of the random coefficient analysis show that the existence of a shopping center in the CBD without parking spaces for bus mode and household car ownership (two or more) for walking mode are the primary sources of heterogeneity of grocery shopping trips. Findings suggest that considering policies like parking management, especially in CBD and development of public transit stations, based consistent with the development of pedestrian facilities, increase the likelihood of using transit. Also, policies like optimal grocery shopping malls sitting in all areas of the city increase the likelihood of using walking mode.

Commuters Perception Towards The Bus Base Park and Ride Implementation in Colombo City Limit

This study was conducted to identify the commuter‟s perception towards the bus base Park and Ride implementation in Colombo city limit. This study analyses the factors that commuters would consider when selecting park and ride system for their travel purpose. For the researcher to achieve the aforementioned objectives, the conceptual framework was structured based on secondary data which congregated by existing publications and articles. The research questionnaire was developed in order to gather primary data through google forms. Simple random sampling technique was used to distribute the questionnaire among the commuters who use seven corridors to enter Colombo city limit. Three hundred fifty-two responses were taken into consideration and analysis was done by taking them into one data base. The reliability of the collected data was analysed using Cronbach‟s alpha. The KMO test statistic for sample adequacy. Moreover, factor analysis was created based on the Principal Component Analysis by extracting 3 factors from the 22 Variables, the total amount of variance accounted, redistributed over the three extracted factors, renamed using Component Score Coefficient Matrix. Kruskal-Wallis Test was conducted for hypothesis testing and binary logistics analysis was conducted to identify the association of factors and for create a model. As per the Kruskal-Wallis test, author has found that most of the demographic variables were impacted to the commuter‟s perception toward the bus base PnR system. Mean values of the 22 variables were concluded that bus frequency, travel time and safety and security of PnR system were mostly influenced for selection of PnR system. Out of the extracted factors, service attribute and travel influences were having positive association toward the commuters ‟perception on PnR choice while comfortability factor was insignificant. As per the research findings, the overall commuters” perception on PnR can be concluded as strong positive perception.

Optimizing the bus operation plan Based on Deep Learning

The bus operation plan is closely to urban resident daily life. A good bus operation plan can improve the travel efficiency and comfort at the same time. To reduce the cost of traveling with bus companies and passengers, here a bus operation model considering shuttle buses based on predicted data (PSBO) is proposed. This model contains three parts, namely the passenger flow prediction, bus travel time prediction, and bus operation optimization. In the passenger flow prediction part, we propose a novel passenger flow prediction model based on long short-term memory (LSTM) and the passenger travel frequency, called Two-stage LSTM passenger flow prediction model (TLPLP). The accuracy of the TLPLP model is verified by comparison with multiple models. This paper also chooses the LSTM model to predict the bus travel time between different stations. The predicted passenger number and bus travel time are two inputs in the bus operation optimization part of PSBO model. This model aims at minimizing the passenger travel time and the operation cost through an insert shuttle buses method. We design a quick algorithm to solve this model and verifies the effect of this model compared with the bus dispatching plan in use.

The relative importance of bus system's Perceived Service Quality (PSQ) attributes among public and private mode users in Indonesia

Importance-Performance Analysis (IPA) is a common approach usually applied in examining public satisfaction and has been adopted in the transportation sector to measure the quality of service provided by the public transport system. This study, therefore, investigated the relative important service quality attributes of bus systems among both public and private modes of transportation users in Banda Aceh in Indonesia. An urban bus system known as the "Trans Koetardja" was used as a case study and a questionnaire designed based on preference survey was applied. A total of 200 samples comprising of 100 bus users and 100 private mode users including cars and motorcycles were used for the preliminary study. Moreover, the IPA approach was used to evaluate the Trans Koetaradja service quality attributes based on importance and performance classification. The quadrant grid plot showed the need for the operators to allocate their resources towards improving their services by considering (a) an improvement in bus stop facilities, (b) enhancement in the route and accurate timetable, and (c) shortening bus travel time and waiting time in the bus stop

Statistical modelling of bus travel time with Burr distribution

A better understanding of the travel time distribution shape or pattern could improve the decision made by the transport operator to estimate the travel time required for the vehicle to travel from one place to another. Finding the most appropriate distribution to represent the day-to-day travel time variation of an individual link of a bus route is the main purpose of this study. Klang Valley, Malaysia is the study area for the research. A consecutive of 7 months ten bus routes automatic vehicle location (AVL) data are used to examine the distribution performance. The leading distribution proposed for the research is the Burr distribution. Both symmetrical and asymmetrical distributions that have been proposed in existing studies are also used for comparison purposes. Maximum likelihood estimation is applied for parameter estimation while loglikelihood value, Akaike information criterion (AIC) and Bayesian information criterion (BIC) are applied for performance assessment of the distributions. Promising results are obtained by the leading model in all different kinds of operating environment and could be treated as the preliminary preparation for further reliability analysis.

Dynamic Thresholds Identification for Green Extension and Red Truncation Strategies for Bus Priority

Among the different strategies adopted to improve the efficiency and reliability of the bus services, bus signal priority is a low cost and less infrastructure- demanding solution that has the potential to reduce bus travel times in urban arterials. This paper develops analytical models for finding the thresholds for Green Extension (GE) and Red Truncation (RT) for a four-phase signal system with buses on conflicting phases. The thresholds are developed based on reducing the total person delay after considering buses from the current cycle and unserved buses from the previous cycle for priority decisions. The proposed models aim for zero-delay service for the buses from the current cycle and reducing delay for the unserved buses from the previous cycle. The models reveal that at multi-phase signals, several bus priority alternatives are possible to reduce total person delay and agencies can choose alternatives based on their requirements and constraints. These models are evaluated in VISSIM microscopic simulation environment. The evaluation results indicate a 16.7 to 42.8% reduction in total person intersection delay due to the implemented bus priority.

The congestion relief benefit of public transit: evidence from Rome

Abstract We estimate the effect of public transport supply on travel times of motor-vehicle and bus users in Rome, Italy. We apply a quasi-experimental methodology exploiting hourly information on public transport service reductions during strikes. We find that a 10-percentage point reduction in public transit supply increases the travel time of motor-vehicles by about 1.6% in the morning peak. The effect on bus travel time is similar. The congestion-relief benefit of public transport is thus sizeable and bus travel time gains account for an important share of it.

Assessment of the impact of the characteristics of the road network on the duration of the route

The “floating” car method using global positioning system (GPS) devices is used to collect data on the duration of traffic on city streets. However, a complete study of the transport network is time-consuming and includes additional financial investments. An alternative source of data collection is the use of rolling stock of public transport. Therefore, this article discusses the relationship between travel time by car and bus. It was found that the travel time of buses is usually more than the travel time of an individual vehicle. The impact of the characteristics of the road network, such as a number of regulated and unregulated intersections, a number of driveways, a number of bus stops, traffic intensity, a number of lanes and a number of turns on the duration of public transport, has been assessed. The results show the relationship between the considered durations of traffic and data from the board equipment of the vehicles of public transport can be used to estimate the duration of movement along the route taking into account the characteristics of the network.

Impact of stops for bus delays on routes

In this paper bus travel time and delay is significant measures in public transportation. This research aims to explore impact of stops for the selected bus route and to find some simple suitable solutions to reduce the travel time and minimize delays for scheduling preferences of travellers. Delays data were collected using automatic vehicle location system (GPS technology) for the bus route #51 in Tashkent. Based on the collected GPS data were proposed the developed a linear and a logistic multivariate regression model. Travel time reliability were estimated regarding to a multivariate regression models and performed. The result shows that very good statistical suitable as expected and thus can be used in the public transportation to determine any travel time using real-time and available offline data. These outcomes, in the future it is expected that will be applicable and more efficient bus public transportation system in Tashkent.

New transit signal priority scheme for intersections with nearby bus rapid transit median stations

To improve the accessibility of bus rapid transit (BRT) systems, the median stations are usually located beside intersections. However, due to the limited storage space at the stations, high bus volumes and fluctuant bus arrival patterns may cause queue spillbacks and consequently block the intersections. Traditional transit signal priority control could deteriorate such situations when a green extension or a red truncation is activated. Hence, it is essential to develop a new operational framework that is capable of both reducing bus travel time and preventing queue spillbacks at the median stations. This study presents a novel signal control scheme to integrate both priority (i.e. green extension and red truncation) and suppression strategies (i.e. green truncation and red extension) for approaching buses. Notably, the suppression control is only implemented to the farside directional traffic. To evaluate the proposed signal control system, this study tests the algorithms on a real BRT network in Jinan, China. Simulation results of multiple experiments show that the developed method has great promise in bus delay reduction and spillback avoidance in the station area. Other further explorations with sensitivity analysis find that the proposed control will be more effective when the signal is able to provide longer priority and suppression durations.

Mapping of bus travel time to traffic stream travel time using econometric modeling

Travel time is one of the most important traffic parameters for travelers, traffic managers, planners, and operators alike. Travel time estimation is a significant component of any intelligent transportation systems (ITS) operations. In countries like India, which are at a nascent stage of ITS deployment, one of the main hurdles is the lack of accurate and automated systems for travel time data collection. However, in most of the cities in India, public transit buses are equipped with global positioning system (GPS) devices providing rich bus travel time data. Privacy issues deter the use of such GPS devices in private vehicles present in the stream. Hence, estimating the stream travel time using buses as probes is a relevant problem. In countries like India, where a heterogeneous and less lane disciplined traffic condition exists and the buses have to share the road space with other vehicles (no separate bus lanes); this mapping of bus travel time to stream travel time becomes challenging. The present study attempts to estimate stream travel time using buses as probes in a heterogeneous and less lane disciplined urban traffic scenario. Preliminary analysis of the collected travel time data indicated significant variability across time and links. Panel modeling can capture both this cross-sectional and time-dependent behavior and hence is used in this study. The estimation model is corroborated with field data and the performance is found to be satisfactory.

Bus Dynamic Travel Time Prediction: Using a Deep Feature Extraction Framework Based on RNN and DNN

Travel time data is an important factor for evaluating the performance of a public transport system. In terms of time and space within the nature of uncertainty, bus travel time is dynamic and flexible. Since the change of traffic status is periodic, contagious or even sudden, the changing mechanism of that is a hidden mode. Therefore, bus travel time prediction is a challenging problem in intelligent transportation system (ITS). Allowing for a large amount of traffic data can be collected at present but lack of precisely-conducting, it is still worth exploring how to extract feature sets that can accurately predict bus travel time from these data. Hence, a feature extraction framework based on the deep learning models were developed to reflect the state of bus travel time. First, the study introduced different historical stages of bus signaling time, taxi speed, the stop identity (ID) of spatial characteristics, and real-time possible arrival time, signified by fourteen spatiotemporal characteristic values. Then, an embedding network is proposed to leverage a wide and deep structure to mate the spatial and temporal data. In order to meet the temporal dependence requirements, an attention mechanism for a Recurrent Neural Network (RNN) was designed in this research in order to capture the temporal information. Finally, a Deep Neural Networks (DNN) was implemented in this research in order to achieve the dynamic bus travel time prediction. Two case studies of Guangzhou and Shenzhen were tested. The results showed that the performance of the algorithm was more efficient than that of the traditional machine-learning model and promoted by 4.82% compared to the deep neural network applied to the initial feature space. Moreover, the study visualized the weighted cost of attention on the bus’s travel time features during a certain running state. Therefore, the study demonstrated the proposed model enabled to understand the characteristic data of transit travel time with visualization.

Predicting Travel Times of Bus Transit in Washington, D.C. Using Artificial Neural Networks

This study aimed to develop travel time prediction models for transit buses to assist decision-makers improve service quality and patronage. Six-months’ worth of Automatic Vehicle Location and Automatic Passenger Counting data for six Washington Metropolitan Area Transit Authority bus routes operating in Washington, DC was used for this study. Artificial Neural Network (ANN) models were developed for predicting travel times of buses for different peak periods. The analysis included variables such as length of route between stops, average dwell time and number of intersections between bus stops amongst others. Quasi-Newton algorithm was used to train the data to obtain the ideal number of perceptron layers that generated the least amount of error for all peak models. Comparison of the Normalized Squared Errors generated during the training process was done to evaluate the models. Travel time equations for buses were obtained for different peaks using ANN. The results indicate that the prediction models can effectively predict bus travel times on selected routes during different peaks of the day with minimal percentage errors. These prediction models can be adapted by transit agencies to provide patrons with more accurate travel time information at bus stops or online. Doi: 10.28991/cej-2020-03091615 Full Text: PDF

Numerical Stability of Conservation Equation for Bus Travel Time Prediction Using Automatic Vehicle Location Data

Travel time is a variable that varies over both time and space. Hence, an ideal formulation should be able to capture its evolution over time and space. A mathematical representation capturing such variations was formulated from first principles, using the concept of conservation of vehicles. The availability of position and speed data obtained from GPS enabled buses provide motivation to rewrite the conservation equation in terms of speed alone. As the number of vehicles is discrete, the speed-based equation was discretized using Godunov scheme and used in the prediction scheme that was based on the Kalman filter. With a limited fleet size having an average headway of 30 min, availability of travel time data at small interval that satisfy the requirement of stability of numerical solution possess a big challenge. To address this issue, a continuous speed fill matrix spatially and temporally was developed with the help of historic data and used in this study. The performance of the proposed Advanced Time-Space Discterization (AdTSD) method was evaluated with real field data and compared with existing approaches. Results show that AdTSD approach was able to perform better than historical average approach with an advantage up to 11% and 5% compared to Base Time Space Discretization (BTSD) approach. Also, from the results it was observed that the maximum deviation in prediction was in the range of 2–3 min when it is predicted 10 km ahead and the error is close to zero when it is predicted a section ahead i.e. when the bus is close to a bus stop, indicating that the prediction accuracy achieved is suitable for real field implementation.

Characterizing Bus Travel Time using Advanced Data Visualization Techniques

With the introduction of various automated sensors, traffic data collection has become easier and huge amount of data are getting accumulated over time. One of the interesting challenges in the field of intelligent transportation systems is to effectively utilize such large-scale database. Making meaningful inferences out of this data by conducting in-depth analyses to identify different patterns/trends followed by the traffic variables can lead to the development of more efficient end-applications. The current study analyzes travel time data obtained from buses fitted with global positioning system devices to understand the temporal and spatial variations in travel time in the city of Chennai. For this, data visualization tools such as tree maps and heat maps were used. From temporal analysis, it was observed that travel times are increasing over the years and it was also observed that there is a discernible pattern in travel between weekdays and weekend. From spatial analysis, it was found that there exists a segment specific characteristic of travel time and certain segments experiencing higher travel times in urban areas particularly at intersections. The findings from the study were further used in demonstrating a possible user application, bus travel time prediction system, based on the identified patterns. Performance analysis showed a combination of inputs from same month last year, day of the week, and traffic conditions performing better for the considered dataset.

A route-planning method for long-distance commuter express bus service based on OD estimation from mobile phone location data: the case of the Changping Corridor in Beijing

With the background of hyperurbanization and a jobs–housing imbalance in Beijing and other megacities in China, this study aims at developing a systematic toolkit of demand estimation and route planning for long-distance commuter bus lines. Taking the express bus services (EBS) in the Changping Corridor in Beijing as an example, this paper presents the use of a route-planning algorithm. Mobile phone location (MPL) data serves as a valid instrument for the origin–destination (OD) estimation, which provides a new perspective to identify the locations of homes and jobs. The OD distribution matrices are specified via geocoded MPL data. To minimize the aggregated travel time and attract potential passengers, this study subsamples long-distance commuting trips among the OD matrices by determining the operation distance threshold. The threshold can be solved by comparing the aggregated travel time of EBS to the travel time of local buses and private cars. Using the distance threshold and other operational parameters, we process an iterative computation to determine the length of routes, number of lines, and stop spacing and location. Compared to local buses and private cars, the planned EBS lines could reduce aggregated travel time by at least 20%. The results demonstrate that the method presented in this study is applicable and flexible. This paper may lead to new research directions for route planning of long-distance commuter buses. The limitations of this study and the future research agenda are also discussed.

Bus Travel Time: Experimental Evidence and Forecasting

Bus travel time analysis plays a key role in transit operation planning, and methods are needed for investigating its variability and for forecasting need. Nowadays, telematics is opening up new opportunities, given that large datasets can be gathered through automated monitoring, and this topic can be studied in more depth with new experimental evidence. The paper proposes a time-series-based approach for travel time forecasting, and data from automated vehicle monitoring (AVM) of bus lines sharing the road lanes with other traffic in Rome (Italy) and Lviv (Ukraine) are used. The results show the goodness of such an approach for the analysis and reliable forecasts of bus travel times. The similarities and dissimilarities in terms of travel time patterns and city structure were also pointed out, showing the need to take them into account when developing forecasting methods.