Influences of Mixed Traffic Flow and Time Pressure on Mistake-Prone Driving Behaviors among Bus Drivers

Bus safety is a matter of great importance in many developing countries, with driving behaviors among bus drivers identified as a primary factor contributing to accidents. This concern is particularly amplified in mixed traffic flow (MTF) environments with time pressure (TP). However, there is a lack of sufficient research exploring the relationships among these factors. This study consists of two papers that aim to investigate the impact of MTF environments with TP on the driving behaviors of bus drivers. While the first paper focuses on violated driving behaviors, this particular paper delves into mistake-prone driving behaviors (MDB). To collect data on MDB, as well as perceptions of MTF and TP, a questionnaire survey was implemented among bus drivers. Factor analyses were employed to create new measurements for validating MDB in MTF environments. The study utilized partial correlation and linear regression analyses with the Bayesian Model Averaging (BMA) method to explore the relationships between MDB and MTF/TP. The results revealed a modified scale for MDB. Two MTF factors and two TP factors were found to be significantly associated with MDB. A high presence of motorcycles and dangerous interactions among vehicles were not found to be associated with MDB among bus drivers. However, bus drivers who perceived motorcyclists as aggressive, considered road users’ traffic habits as unsafe, and perceived bus routes’ punctuality and organization as very strict were more likely to exhibit MDB. Moreover, the results from the three MDB predictive models demonstrated a positive impact of bus route organization on MDB among bus drivers. The study also examined various relationships between the socio-demographic characteristics of bus drivers and MDB. These findings are of practical significance in developing interventions aimed at reducing MDB among bus drivers operating in MTF environments with TP.

Traffic Incident Management Performance Measures: Ranking Agencies on Roadway Clearance Time

This study develops a procedure to rank agencies based on their incident responses using roadway clearance times for crashes. This analysis is not intended to grade agencies but to assist in identifying agencies requiring more training or resources for incident management. Previous NCHRP reports discussed usage of different factors including incident severity, roadway characteristics, number of lanes involved and time of incident separately for estimating the performance. However, it does not tell us how to incorporate all the factors at the same time. Thus, this study aims to account for multiple factors to ensure fair comparisons. This study used 149,174 crashes from Iowa that occurred from 2018 to 2021. A Tobit regression model was used to find the effect of different variables on roadway clearance time. Variables that cannot be controlled directly by agencies such as crash severity, roadway type, weather conditions, lighting conditions, etc., were included in the analysis as it helps to reduce bias in the ranking procedure. Then clearance time of each crash is normalized into a base condition using the regression coefficients. The normalization makes the process more efficient as the effect of uncontrollable factors has already been mitigated. Finally, the agencies were ranked by their average normalized roadway clearance time. This ranking process allows agencies to track their performance of previous crashes, can be used in identifying low performing agencies that could use additional resources and training, and can be used to identify high performing agencies to recognize for their efforts and performance.

A Discrete-Time Stochastic Traffic Assignment Model

A discrete time stochastic traffic assignment model is proposed. The model provides a discrete time description of the variations of flows on a road network during a day or a peak period. The congestion effect at links and link junctions are taken into account. The first in first out principle is enforced on all links at all periods of the day. A stochastic user equilibrium assignment is achieved when the tripmaker is unable to find better travel alternatives. A computational procedure is also presented.

Bluetooth as a traffic sensor for stream travel time estimation under Bogazici Bosporus conditions in Turkey

Travel time estimation is an integral part of Intelligent Transportation Systems, and has been an important component in traffic management and operations for many years. Travel time, being spatial in nature, requires spatial sensors to measure it accurately. Bluetooth is emerging as a promising technology for the direct measurement of travel time, and is reported in a few studies from homogenous traffic conditions. At the same time, there have been no studies on the applicability of Bluetooth for travel time estimation in heterogeneous traffic seen in Istanbul and even that Turkey. Bluetooth data collected from a busy urban road in Istanbul city have been analyzed and the penetration rate was found to be about 5 %. Two wheelers and light motor vehicles have been detected using the Bluetooth sensor and the data have been extrapolated to estimate travel times of other classes of vehicles. The study developed linear relationships between speeds of different classes of vehicles through weighted linear regression methods and were used for the estimation of stream travel time. The results obtained were promising and show that Bluetooth is a cost-effective technology for estimation of travel time for heterogeneous traffic conditions.

Small-time scale network traffic prediction based on a local support vector machine regression model

In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.

Screening and reconstruction of real-time traffic data

The quality of real time traffic information is of the great importance, therefore the factors having effect on traffic characteristics are analyzed in general, and the necessities of real time data processing are summarized. The identification and reconstruction of real time traffic data are analyzed using Kalman filter equation and statistical approach. Four methods for ITS (Intelligent transportation system) detector data screening in traffic management systems are discussed in detail. Meanwhile traffic data examinations are discussed with solutions formulated through analysis, and recommendations are made for information collection and data management in future.

A Discrete-Time Traffic and Topology Adaptive Routing Algorithm for LEO Satellite Networks

“Minimizing path delay” is one of the challenges in low Earth orbit (LEO) satellite network routing algo-rithms. Many authors focus on propagation delays with the distance vector but ignore the status information and processing delays of inter-satellite links. For this purpose, a new discrete-time traffic and topology adap-tive routing (DT-TTAR) algorithm is proposed in this paper. This routing algorithm incorporates both inher-ent dynamics of network topology and variations of traffic load in inter-satellite links. The next hop decision is made by the adaptive link cost metric, depending on arrival rates, time slots and locations of source-destination pairs. Through comprehensive analysis, we derive computation formulas of the main per-formance indexes. Meanwhile, the performances are evaluated through a set of simulations, and compared with other static and adaptive routing mechanisms as a reference. The results show that the proposed DT-TTAR algorithm has better performance of end-to-end delay than other algorithms, especially in high traffic areas.

Scheduling of high-speed rail traffic based on discrete-time movement model

In this paper, a new simulation approach for solving the mixed train scheduling problem on the high-speed double-track rail line is presented. Based on the discrete-time movement model, we propose control strategies for mixed train movement with different speeds on a high-speed double-track rail line, including braking strategy, priority rule, travelling strategy, and departing rule. A new detailed algorithm is also presented based on the proposed control strategies for mixed train movement. Moreover, we analyze the dynamic properties of rail traffic flow on a high-speed rail line. Using our proposed method, we can effectively simulate the mixed train schedule on a rail line. The numerical results demonstrate that an appropriate decrease of the departure interval can enhance the capacity, and a suitable increase of the distance between two adjacent stations can enhance the average speed. Meanwhile, the capacity and the average speed will be increased by appropriately enhancing the ratio of faster train number to slower train number from 1.

Traffic Forecasting and Planning of WiMAX under Multiple Priority Using Fuzzy Time Series Analysis

Network traffic prediction plays a fundamental role in characterizing the network performance and it is of significant interests in many network applications, such as admission control or network management. Therefore, The main idea behind this work, is the development of a WIMAX Traffic Forecasting System for predicting traffic time series based on the daily and monthly traffic data recorded (TRD) with association of feed forward multi-layer perceptron (FFMLP). The quality of forecasting WIMAX Traffic obtained by comparing different configurations of the FFMLP that consist of investigating different FFMLP model architectures and different Learning Algorithms. The decision of changing the FFMLP architecture is essentially based on prediction results to obtain the FFMLP model for flow traffic prediction model. The different configurations were tested using daily and monthly real traffic data recorded at each of the two base stations (A and B) that belongs to a Libyan WiMAX Network. We evaluate our approach with statistical measurement and a good statistic measure of FMLP indicates the performance of selected neural network configuration. The developed system indicates promising results in which it successfully network traffic prediction through daily and monthly traffic data recorded (TRD) association with artificial neural network.

Multi-Objective Optimization of Traffic Signal Timing at Typical Junctions Based on Genetic Algorithms

With the rapid development of urban road traffic and the increasing number of vehicles,how to alleviate traffic congestion is one of the hot issues that need to be urgently addressed in building smart cities.Therefore,in this paper,a nonlinear multi-objective optimization model of urban intersection signal timing based on a Genetic Algorithm was constructed.Specifically,a typical urban intersection was selected as the research object,and drivers’acceleration habits were taken into account.What’s more,the shortest average delay time,the least average number of stops,and the maximum capacity of the intersection were regarded as the optimization objectives.The optimization results show that compared with the Webster method when the vehicle speed is 60 km/h and the acceleration is 2.5 m/s^(2),the signal intersection timing scheme based on the proposed Genetic Algorithm multi-objective optimization reduces the intersection signal cycle time by 14.6%,the average vehicle delay time by 12.9%,the capacity by 16.2%,and the average number of vehicles stop by 0.4%.To verify the simulation results,the authors imported the optimized timing scheme into the constructed Simulation of the Urban Mobility model.The experimental results show that the authors optimized timing scheme is superior to Webster’s in terms of vehicle average loss time reduction,carbon monoxide emission,particulate matter emission,and vehicle fuel consumption.The research in this paper provides a basis for Genetic algorithms in traffic signal control.

Travel time prediction with viscoelastic traffic model

Travel time through a ring road with a total length of 80 km has been predicted by a viscoelastic traffic model(VEM), which is developed in analogous to the non-Newtonian fluid flow. The VEM expresses a traffic pressure for the unfree flow case by space headway, ensuring that the pressure can be determined by the assumption that the relevant second critical sound speed is exactly equal to the disturbance propagation speed determined by the free flow speed and the braking distance measured by the average vehicular length. The VEM assumes that the sound speed for the free flow case depends on the traffic density in some specific aspects, which ensures that it is exactly identical to the free flow speed on an empty road. To make a comparison, the open Navier-Stokes type model developed by Zhang(ZHANG, H. M. Driver memory, traffic viscosity and a viscous vehicular traffic flow model. Transp. Res. Part B, 37, 27–41(2003)) is adopted to predict the travel time through the ring road for providing the counterpart results.When the traffic free flow speed is 80 km/h, the braking distance is supposed to be 45 m,with the jam density uniquely determined by the average length of vehicles l ≈ 5.8 m. To avoid possible singular points in travel time prediction, a distinguishing period for time averaging is pre-assigned to be 7.5 minutes. It is found that the travel time increases monotonically with the initial traffic density on the ring road. Without ramp effects, for the ring road with the initial density less than the second critical density, the travel time can be simply predicted by using the equilibrium speed. However, this simpler approach is unavailable for scenarios over the second critical.

Real-Time Traffic Signal Timing for Urban Road Multi-Intersection

this paper develops a real-time traffic signal timing model which is to be integrated into a single intersection for urban road, thereby solving the problem of traffic congestion. We analyze the current situation of the traffic flow with release matrix firstly, and then put forward the basic models to minimize total delay time of vehicles at the intersection. The optimal real-time signal timing model (non-fixed cycle and non-fixed split) is built with the Webster split optimal model. At last, the simulated results, which are compared with conventional model, manifest the promising properties of proposed model.

Novel Real-Time System for Traffic Flow Classification and Prediction

Traffic flow prediction has been applied into many wireless communication applications(e.g., smart city, Internet of Things). With the development of wireless communication technologies and artificial intelligence, how to design a system for real-time traffic flow prediction and receive high accuracy of prediction are urgent problems for both researchers and equipment suppliers. This paper presents a novel real-time system for traffic flow prediction. Different from the single algorithm for traffic flow prediction, our novel system firstly utilizes dynamic time wrapping to judge whether traffic flow data has regularity,realizing traffic flow data classification. After traffic flow data classification, we respectively make use of XGBoost and wavelet transform-echo state network to predict traffic flow data according to their regularity. Moreover, in order to realize real-time classification and prediction, we apply Spark/Hadoop computing platform to process large amounts of traffic data. Numerical results show that the proposed novel system has better performance and higher accuracy than other schemes.

道路环形交叉口机动车冲突风险区识别模型研究

为定量识别城市非信控环形交叉口区域内的机动车冲突风险易发生点,降低环形交叉口的事故发生率,本文构建针对非信控环形交叉口机动车冲突风险识别模型。首先,利用无人机采集高精度、连续的多车辆轨迹视频,结合Kinovea视频运动分析软件实现运行车辆状态识别与跟踪,并记录车辆每一帧的运动数据;其次,基于交通冲突识别指标TTC(Time to Collision),提出适应环形交叉口道路线形特征的车辆TTC计算方法,并使用累计频率法确定严重、一般和轻微冲突的阈值分别为1.2,2.8,4.4 s;最后,通过绘制高峰和平峰交通冲突空间异步图,并结合交通冲突数和严重冲突率,对环形交叉口的36个子区段进行交通冲突风险等级评定。研究结果显示:在高峰时段,某一子区段的平均交通冲突发生次数约为15次,严重冲突率为17.45%;在平峰时段,某一子区段的平均交通冲突发生次数约为8次,严重冲突率为8.28%。重度风险区域在高峰时段占比达到50%,而在平峰时段为8.33%,这些重度风险区域主要集中在交织区段。因此,环形交叉口在高峰时段且位于交织区段的情况更易发生交通事故。本文研究成果有助于交通管理部门了解环形交叉口在不同时段和区段上的交通冲突情况和特征,以便采取相应的预警和管理措施。

船舶交通数据实时传输可靠性评估系统

为保障船舶交通数据实时传输的可靠性,设计船舶交通数据实时传输可靠性评估系统。从网络条件、节点设备、环境因素与网络管理4个主要方面出发选取评估指标,构建船舶交通数据实时传输可靠性评估指标体系;采用基于三角模糊数的模糊层次分析法计算评估指标的权重值;将各评估指标与对应的权重值形成并进行汇总,由此得到最终船舶交通数据实时传输可靠性评估结果。实验结果显示,该系统能够获取准确的评估结果,且由于不同因素造成的船舶交通数据实时传输故障发生概率下降幅度均达到50%以上,由此说明基于该系统的评估结果能够有效保障船舶交通数据实时传输的可靠性。

考虑健康信念的游客分时预约行为分析

为探索新冠肺炎疫情背景下景区分时预约策略、游客健康信念和风险预防行为之间的关系,需考虑健康信念,分析疫情防控期间游客分时预约行为机理。采用意愿调查法获取旅游信息、健康信念、分时预约行为意向数据,分析各预约时段余票比例和门票预售期等信息对游客分时预约行为的影响。基于Nested Logit(NL)模型,建立考虑健康信念的游客游玩日期-时段预约选择联合模型,并进行敏感性分析。结果表明:预约信息、健康信念对游客预约节假日或非节假日游玩有显著影响;票价、各预约时段余票比例和健康信念对游客游玩时段预约选择行为有显著影响。游客感知新冠的严重性由1上升至5时,选择节假日出行的概率由30.46%下降至12.28%。研究可为景区分时预约策略的制定提供参考依据。

民航客运量预测方法研究综述

为了提升民航运行效率、准确预测客运量,促进其可持续发展,采用分类学方法将民航客运量预测方法划分为传统统计学、机器学习、组合模型3大类。详述各类方法的改进原理、效果和应用,通过数据处理、权重调整、参数优化和结构改进提高准确性,并总结组合模型相对单一模型的优势。实证研究结果表明,组合预测模型相较于单一模型具有更高的准确性,并指出结合人工智能和大数据技术的发展趋势,构建优秀的组合预测模型将是提高准确性的潜在研究方向。

基于主线密度预测的快速路入口匝道控制方法

针对快速路入口匝道区域拥堵问题,本文提出一种基于交通密度预测的入口匝道控制方法。通过分析快速路匝道区域的交通特性,定义交通流缓行状态,利用经典宏观交通流模型对快速路主线的历史密度进行统计,基于速度、密度参数进行聚类分析量化缓行状态交通参数特征。基于城市交通流的日变化特性,利用时间序列模型对交通密度进行短期预测,基于预测结果识别缓行状态。针对交通缓行状态,提出一种快速路主线交通密度与入口匝道排队长度的协同控制模型,通过控制入口匝道进而控制主线下游密度不超过临界密度,利用PSO-PID(Particle Swarm Optimization-Proportional Integral Derivative Control)算法对控制模型进行求解。以南昌市洪都大道快速路为例,对晚高峰缓行时段进行仿真分析,并与经典的匝道控制方案进行对比分析。结果表明:本文模型相较ALINEA模型,将匝道调节率提高11.0%,匝道平均排队长度和平均延误分别降低了14.8%和13.5%。

基于YOLO模型的车流量实时采集系统研究

对于一座现代化城市来说,合理的交通规划是一个城市高效运行的关键,作为交通规划的关键信息的城市车流量信息,原本需要人工进行识别、获取、验证的提取方式,随着计算机视觉技术的蓬勃发展弊端尽显,终将退出历史的舞台。为了提高城市车流量信息的准确性和及时性,利用现有的计算机技术设计一种基于YOLO模型的车流量实时采集系统。该系统基于YOLO视觉检测模型,采用DeepSORT算法对检测到的目标车辆进行跟踪识别、判断车辆的运行状态、实现当前路段的车流量统计、对已记录车流量信息进行可视化展示以及数据输出等。该系统可以有效地代替传统消耗人力的死板工作,实现自动化数据收集以及道路交通情况的快速监测。该系统操作简单,交互性强,为城市的交通管理和交通规划提供准确实时的信息数据。