A New Big Data Approach to Understanding General Traffic Impacts on Bus Passenger Delays

Montero-Lamas, Yaiza; Novales, Margarita; Orro, Alfonso; Currie, Graham

doi:10.1155/2023/4082587

Cited by 3 publications

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Predicting vehicle travel time on city streets for trip preplanning and predicting heavy traffic for proactive control of street congestion

Nofal

2024

Sci Rep

View full text Add to dashboard Cite

We investigate if the vehicle travel time after 6 h on a given street can be predicted, provided the hourly vehicle travel time on the street in the last 19 h. Likewise, we examine if the traffic status (i.e., low, mild, or high) after 6 h on a given street can be predicted, provided the hourly traffic status of the street in the last 19 h. To pursue our objectives, we exploited historical hourly traffic data from Google Maps for a main street in the capital city of Jordan, Amman. We employ several machine learning algorithms to construct our predictive models: neural networks, gradient boosting, support vector machines, AdaBoost, and nearest neighbors. Our experimental results confirm our investigations positively, such that our models have an accuracy of around 98–99% in predicting vehicle travel time and traffic status on our study’s street for the target hour (i.e., after 6 h from a specific point in time). Moreover, given our time series traffic data and our constructed predictive models, we inspect the most critical indicators of street traffic status and vehicle travel time after 6 h on our study’s street. However, as we elaborate in the article, our predictive models do not agree on the degree of importance of our data features.

show abstract

Predicting vehicle travel time on city streets for trip preplanning and predicting heavy traffic for proactive control of street congestion

Nofal

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

A Dual-View Approach for Multistation Short-Term Passenger Flow Prediction in Bus Transit Systems

Luo,

Kuang,

Zhang

et al. 2023

Journal of Advanced Transportation

View full text Add to dashboard Cite

Timely and accurate prediction of bus passenger flow plays a crucial role in uncovering real-time traffic demand, presenting an essential and formidable challenge in the realm of bus scheduling and management. The extensive application of deep learning methods in transit passenger flow prediction can be attributed to their exceptional ability to effectively capture spatiotemporal features, resulting in superior performance. However, prevailing deep learning models in transit passenger flow prediction tend to ignore the data enhancement. Additionally, the predominant focus on a single station in the prediction task presents challenges in effectively capturing the spatiotemporal features of the entire network. A model named TSD-ST is proposed to better accomplish the task of predicting short-term transit passenger flow at multistation. The TSD-ST model leverages time series decomposition for data enhancement. Simultaneously, in addition to considering the adjacency graph, the similarity of all the stations of the entire transit network is also considered and uses multigraph convolution and graph fusion modules. This approach enables the TSD-ST model to effectively capture spatiotemporal dependencies. Experiments based on real-world bus transit datasets confirm that the TSD-ST model shows better performance in prediction tasks at 30-min, 60-min, and 90-min time scales, with an average improvement of 21.87%. The effectiveness of each component has been verified through ablation experiments.

show abstract

Reinforcement Learning for Traffic Control using social preferences

Barzilai

2024

Civil Engineering

View full text Add to dashboard Cite

Traffic congestion arises from all directions, particularly during peak hours, and requires the implementation of a preference mechanism—designated lanes are set up as fast lanes for prioritizing public transportation and ride sharing. Defining a rigid criterion for using the fast lanes can be ineffective if the criterion for using these lanes is unrelated to traffic volume. In situations where fast lanes become overloaded, the rigid criteria do not ensure efficient travel. A social preference criterion, similar to those utilized in priority queues found in various service sectors such as government, travel, and cultural events, could be adapted for use in managing traffic flow and lane prioritization. The social preference criteria will be based on the driver’s characteristics (e.g., a handicraft driver) or not its travel purpose (e.g., a doctor traveling for emergency surgery). To facilitate efficient travel for vehicles utilizing the fast lanes, the implementation of a reinforcement learning (RL) algorithm, specifically the Q-learning algorithm, is proposed. The results indicated that individuals exhibit social preference for various categories of vehicle passenger characteristics. The Q-learning algorithm regulated traffic flow in a junction simulation, distinguishing between fast lanes and regular lanes based on both social preference and traffic volume. This approach ensured efficient prioritization and allocation of resources.

show abstract

A New Big Data Approach to Understanding General Traffic Impacts on Bus Passenger Delays

Cited by 3 publications

References 35 publications

Predicting vehicle travel time on city streets for trip preplanning and predicting heavy traffic for proactive control of street congestion

Predicting vehicle travel time on city streets for trip preplanning and predicting heavy traffic for proactive control of street congestion

A Dual-View Approach for Multistation Short-Term Passenger Flow Prediction in Bus Transit Systems

Reinforcement Learning for Traffic Control using social preferences

Contact Info

Product

Resources

About