scholarly journals A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities

2021 ◽  
Vol 7 ◽  
pp. e689
Author(s):  
Asad Abdi ◽  
Chintan Amrit
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Selection Process ◽  
Influence Factors ◽  
Transportation Systems ◽  
Future Research ◽  
Extensive Literature ◽  
Travel Time Prediction ◽  
Significant Information ◽  
Time Prediction

Transportation plays a key role in today’s economy. Hence, intelligent transportation systems have attracted a great deal of attention among research communities. There are a few review papers in this area. Most of them focus only on travel time prediction. Furthermore, these papers do not include recent research. To address these shortcomings, this study aims to examine the research on the arrival and travel time prediction on road-based on recently published articles. More specifically, this paper aims to (i) offer an extensive literature review of the field, provide a complete taxonomy of the existing methods, identify key challenges and limitations associated with the techniques; (ii) present various evaluation metrics, influence factors, exploited dataset as well as describe essential concepts based on a detailed analysis of the recent literature sources; (iii) provide significant information to researchers and transportation applications developer. As a result of a rigorous selection process and a comprehensive analysis, the findings provide a holistic picture of open issues and several important observations that can be considered as feasible opportunities for future research directions.

scholarly journals Intelligent Traffic Management: A Review of Challenges, Solutions, and Future Perspectives

2021 ◽  
Vol 22 (2) ◽  
pp. 163-182
Author(s):  
Roopa Ravish ◽  
Shanta Ranga Swamy
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Transportation Systems ◽  
Travel Time Prediction ◽  
Novel Technologies ◽  
Related Data ◽  
Traffic Systems ◽  
And Control ◽  
Future Work

Abstract Recent years have witnessed a colossal increase of vehicles on the roads; unfortunately, the infrastructure of roads and traffic systems has not kept pace with this growth, resulting in inefficient traffic management. Owing to this imbalance, traffic jams on roads, congestions, and pollution have shown a marked increase. The management of growing traffic is a major issue across the world. Intelligent Transportation Systems (ITS) have a great potential in offering solutions to such issues by using novel technologies. In this review, the ITS-based solutions for traffic management and control have been categorized as traffic data collection solutions, traffic management solutions, congestion avoidance solutions, and travel time prediction solutions. The solutions have been presented along with their underlying technologies, advantages, and drawbacks. First, important solutions for collecting traffic-related data and road conditions are discussed. Next, ITS solutions for the effective management of traffic are presented. Third, key strategies based on machine learning and computational intelligence for avoiding congestion are outlined. Fourth, important solutions for accurately predicting travel time are presented. Finally, avenues for future work in these areas are discussed.

scholarly journals Research on Expressway Travel Time Prediction Based on Exclusive Disjunctive Soft Set

2020 ◽  
Vol 308 ◽  
pp. 02005
Author(s):  
Qingqing Wang ◽  
Huamin Li ◽  
Weixin Xiong
Keyword(s):  
Travel Time ◽  
Set Theory ◽  
Road Traffic ◽  
Influence Factors ◽  
Soft Set ◽  
Travel Time Prediction ◽  
Time Model ◽  
Traffic System ◽  
The Road ◽  
Time Prediction

In order to study the prediction problem of expressway travel time, due to the ambiguity and uncertainty in the road traffic system, the travel time prediction model is established based on the exclusive disjunctive soft set theory. Through the parameter reduction theory of soft set, the main influence factors are extracted, and the mapping relationship between the influence factors and the travel time is obtained through the exclusive disjunctive soft set decision system. The travel time model is established based on the soft set theory, and the travel time is calculated through the mapping relationship. The experimental results show that, compared with the BPR function model, the travel time model based on the exclusive disjunctive soft set theory reduces the prediction error and effectively improves the calculation accuracy of the travel time.

scholarly journals Towards Attention-Based Convolutional Long Short-Term Memory for Travel Time Prediction of Bus Journeys

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3354 ◽  
Author(s):  
Jianqing Wu ◽  
Qiang Wu ◽  
Jun Shen ◽  
Chen Cai
Keyword(s):  
Information Systems ◽  
Travel Time ◽  
Short Term Memory ◽  
Transportation Systems ◽  
Series Data ◽  
Travel Time Prediction ◽  
Short Term ◽  
Term Memory ◽  
Time Prediction ◽  
Long Short Term Memory

Travel time prediction is critical for advanced traveler information systems (ATISs), which provides valuable information for enhancing the efficiency and effectiveness of the urban transportation systems. However, in the area of bus trips, existing studies have focused on directly using the structured data to predict travel time for a single bus trip. For state-of-the-art public transportation information systems, a bus journey generally has multiple bus trips. Additionally, due to the lack of study on data fusion, it is even inadequate for the development of underlying intelligent transportation systems. In this paper, we propose a novel framework for a hybrid data-driven travel time prediction model for bus journeys based on open data. We explore a convolutional long short-term memory (ConvLSTM) model with a self-attention mechanism that accurately predicts the running time of each segment of the trips and the waiting time at each station. The model is more robust to capture long-range dependence in time series data as well.

Influence Mechanism of Travel Information Service Level for Drivers' Travel Time Prediction Error

2014 ◽  
Vol 505-506 ◽  
pp. 1183-1188
Author(s):  
Neng Wan ◽  
Jian Xiong ◽  
Feng Xiang Guo
Keyword(s):  
Travel Time ◽  
Prediction Error ◽  
Influence Factors ◽  
Information Service ◽  
Service Level ◽  
Travel Time Prediction ◽  
Travel Information ◽  
The Road ◽  
Time Prediction ◽  
Planning And Construction

In order to reveal the effect mechanism of travel information service level for drivers travel time prediction error, defined the concept of travel information service level and travel time prediction error. Utilize the conceptual model, described the various influence factors of travel information service level and interaction relations. Discussed the relationship between the drivers travel information receiving preference habits and the road selection, analyzed the effect of the influence factors on drivers' road selection and travel time prediction, based on Bayesian methods analyzed the effect of different travel information service level for travel time prediction error. The calculation shows that the higher travel information service level can improve the drivers travel time prediction, increase the travel information service level play an important role for the efficiency of drivers travel, and provide theoretical support for planning and construction of travel information system on the future.

Modeling of BRT System Travel Time Prediction Using AVL Data and ANN Approach

2021 ◽  
pp. 1-16
Author(s):  
Milad Baradaran Shahidin
Keyword(s):  
Regression Model ◽  
Travel Time ◽  
Public Transportation ◽  
Transportation Systems ◽  
Linear Regression Method ◽  
Ann Model ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Artificial Neural Network Ann ◽  
High Level

Improving the quality of public transportation systems and encouraging passengers to use them are effective solutions for reducing transportation problems in metropolitan. Prediction of travel time and providing information to passengers are significant factors in this process. In this research not only the travel time components in Bus Rapid Transit (BRT) system were investigated but also an Artificial Neural Network (ANN) model and a regression model for travel time prediction were presented. To enhance this aim, data was collected by AVL data and field observation and after investigating the primary independent variables, the significant ones were determined using statistical analysis, then ANN development was done. Moreover, linear regression method was used for this purpose. The results prove that although both models have high level of prediction accuracy, ANN model outperform the regression model and the accuracy for the route sections with no signalized intersections is higher than the others.

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