Transit Signal Priority (TSP) strategy gives public transit vehicles privileges to pass through the intersection without stopping. Most previous studies have adopted the compulsory TSP strategy that ...considers to maximize the utility of public transportation, which is likely to reduce the efficiency of social vehicles. In this paper, we propose an Adaptive Transit Signal Priority (ATSP) model that considers the efficiency of both buses and social vehicles. This model has the Single Request Adaptive Transit Signal Priority (SR-ATSP) module and the Multi-Request Adaptive Transit Signal Priority (MR-ATSP) module. First, the intersection network is divided into grids based on the Discrete Traffic State Encoding (DTSE) idea to obtain the spatial information of vehicles. Then, in the SR-ATSP module, the Dueling Double Deep Q-learning Network (D3QN) algorithm is introduced to determine whether to implement the TSP strategy or not, considering the goal of minimizing the total passenger waiting time of buses and social vehicles. Based on the SR-ATSP, the MR-ATSP module introduces some rules to tackle the conflict from multiple priority requests of different buses. Simulation experiments based on an intersection in Nansha District, Guangzhou City are conducted on SUMO software. The results show that the proposed ATSP model can realize the priority treatment for
of buses while reducing the waiting time of social vehicles by
. It has superior performance for reducing the waiting time of buses and social vehicles than other widely-used TSP models.
Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic ...framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative mechanism of spatiotemporal trip records of each traveler. This model introduces two separate factor matrices for the spatial dimension and the temporal dimension, respectively, and use a two-dimensional core structure at the individual level to effectively model the joint interactions and complex dependencies. This model can efficiently summarize travel behavior patterns on both spatial and temporal dimensions from very sparse trip sequences in an unsupervised way. In this way, complex travel behavior can be modeled as a mixture of representative and interpretable spatiotemporal patterns. By applying the trained model on future/unseen spatiotemporal records of a traveler, we can detect her behavior anomalies by scoring those observations using perplexity. We demonstrate the effectiveness of the proposed modeling framework on a real-world license plate recognition (LPR) data set. The results confirm the advantage of statistical learning methods in modeling sparse individual travel behavior data. This type of pattern discovery and anomaly detection applications can provide useful insights for traffic monitoring, law enforcement, and individual travel behavior profiling.
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.
Both environment protection and energy saving have attracted more and more attention in the electric vehicles (EVs) field. In fact, regarding control performance, electric motor has more advantages ...over conventional internal combustion engine. To decouple the interaction force between vehicle and various coordinating and integrating active control subsystems and estimate the real-time friction force for Advanced Emergency Braking System (AEBS), this paper’s primary intention is uniform distribution of longitudinal tire-road friction force and control strategy for a Novel Anti-lock Braking System (Nov- ABS) which is designed to estimate and track not only any tire-road friction force, but the maximum tire-road friction force, based on the Anti-Lock Braking System (ABS). The longitudinal tire-road friction force is computed through real-time measurement of breaking force and angular acceleration of wheels. The Magic Formula Tire Model can be expressed by the reference model. The evolution of the tire-road friction is described by the constrained active-set SQP algorithm with regard to wheel slip, and as a result, it is feasible to identify the key parameters of the Magic Formula Tire Model. Accordingly, Inverse Quadratic Interpolation method is a proper way to estimate the desired wheel slip in regards to the reference of tireroad friction force from the top layer. Then, this paper adapts the Nonlinear Sliding Mode Control method to construct proposed Nov-ABS. According to the simulation results, the objective control strategy turns out to be feasible and satisfactory.
Both environment protection and energy saving have attracted more and more attention in the electric vehicles (EVs) field. In fact, regarding control performance, electric motor has more advantages ...over conventional internal combustion engine. To decouple the interaction force between vehicle and various coordinating and integrating active control subsystems and estimate the real-time friction force for Advanced Emergency Braking System (AEBS), this paper’s primary intention is uniform distribution of longitudinal tire-road friction force and control strategy for a Novel Anti-lock Braking System (Nov- ABS) which is designed to estimate and track not only any tire-road friction force, but the maximum tire-road friction force, based on the Anti-Lock Braking System (ABS). The longitudinal tire-road friction force is computed through real-time measurement of breaking force and angular acceleration of wheels. The Magic Formula Tire Model can be expressed by the reference model. The evolution of the tire-road friction is described by the constrained active-set SQP algorithm with regard to wheel slip, and as a result, it is feasible to identify the key parameters of the Magic Formula Tire Model. Accordingly, Inverse Quadratic Interpolation method is a proper way to estimate the desired wheel slip in regards to the reference of tireroad friction force from the top layer. Then, this paper adapts the Nonlinear Sliding Mode Control method to construct proposed Nov-ABS. According to the simulation results, the objective control strategy turns out to be feasible and satisfactory. KCI Citation Count: 12
The research reported in this paper quantifies the impact of rainfall on traffic operation of urban road network. The macroscopic analysis method of the network is the Macroscopic Fundamental Diagram ...(MFD), which can be used to describe and estimate the level-of-service of road network and evaluate the network-wide traffic state. Quantitative mastering the impact of rainfall on MFD of urban network is not only important for the further understanding of the MFD stability, but also important for the transportation planning and traffic management under rainy conditions. The results of the empirical analysis indicate that rainfall has an obviously diminishing effect on traffic variables of the network's MFD. The average reductions in production, accumulation and weighted speed are 0.2%, 7.8% and 5.4%, respectively. Moreover, rain has greater negative impact on network's MFD in the evening peak.
The prediction of bus travel time is one of the key of public traffic guidance, accurate bus arrival time information is vital to passengers for reducing their anxieties and waiting times at bus ...stop, or make reasonable travel arrangement before a trip. Research aim at bus travel time prediction is comprehensive at home and abroad. This paper proposes a model to combine road traffic state with bus travel to form the Bayesian network, with a lot of historical data, the parameter of network can be achieved, through estimating the real-time traffic status, so as to predict the bus travel time. We introduced Markov transfer matrix to forecast the traffic state, and substitute the estimate state value into the joint distribution of bus travel time and state, the real time bus travel time predicted value can be obtained. Bus travel time predicted by the proposed model is assessed with data of transit route 69 in Guangzhou between two bus stops, the results show that the proposed model is feasible, but the accuracy needs to be further improved.
This paper presents two adaptive two-stage fuzzy controllers for traffic signals at isolated intersections. Firstly, a two-stage combination fuzzy controller is designed. For traffic status variables ...in two-stage controller leading to the inefficiency of traffic states weakening under low traffic flow, the controller introduces 0-1 combination and determines the combination of traffic status variables of fuzzy controller's inputs from the perspective of structural optimization. Secondly, aiming at the problems of fuzzy controller parameter empirical settings and functional disability of learning, a two-stage fuzzy logic traffic signal controller with online optimization is proposed; this controller introduces the rolling horizon framework and optimizes the parameters of membership functions and controller rules by using hybrid genetic algorithm. The performance of two proposed models is validated via online Paramics-based simulation platform, and extensive simulation tests have demonstrated the potential of developed controllers for adaptive traffic signal control.
The premise of relieving urban traffic congestion is to quantitatively master and evaluate the traffic operating performance of urban road network. In the paper, an observation-based model ...Macroscopic Fundamental Diagram (MFD) is applied to describe and evaluate the traffic state, which connecting the accumulation and production. It should be highlighted that a traffic parameter, network operation efficiency, is introduced to divide the traffic state into three categories: free flow conditions, optimal accumulation and congestion. The empirical analysis is carried out on a core urban road network located in Haizhu District of Guangzhou, China. The results verify that an MFD exists for the complete network and can be used to evaluate the traffic operation of the district. The results also show that the road network being saturated only when the accumulation reaching 477pcu and the production being 600pcu/h. Moreover, the weighted free-flow speed is 43.9km/h, and the weighted jam density is 72.1 vehicles/km.
PDF
