Factors Influence Hanoians’ Choice of Non-Motorized Transport Mode to Access Bus Stations

Hanoi’s rapid urbanization has led to a surge in private vehicle ownership, particularly motorcycles, amidst inadequate public transportation infrastructure. Despite government efforts, many still prefer motorized transport, citing mobility and safety concerns, exacerbated by insufficient pedestrian infrastructure. This study examines the motivations behind this reliance on motorized vehicles, particularly motorcycles, in Hanoi. Findings reveal safety and convenience as primary factors driving motorized transport use, especially for accessing bus stations. Economic incentives could promote non-motorized travel and public transport adoption. Policy implications highlight the importance of addressing economic factors and improving access infrastructure to manage motorized vehicle reliance and foster sustainable urban mobility in Hanoi.

Pedestrian lane formation with following–overtaking model and measurement of system order

Pedestrian self-organizing movement plays a significant role in evacuation studies and architectural design.Lane formation,a typical self-organizing phenomenon,helps pedestrian system to become more orderly,the majority of following behavior model and overtaking behavior model are imprecise and unrealistic compared with pedestrian movement in the real world.In this study,a pedestrian dynamic model considering detailed modelling of the following behavior and overtaking behavior is constructed,and a method of measuring the lane formation and pedestrian system order based on information entropy is proposed.Simulation and analysis demonstrate that the following and avoidance behaviors are important factors of lane formation.A high tendency of following results in good lane formation.Both non-selective following behavior and aggressive overtaking behavior cause the system order to decrease.The most orderly following strategy for a pedestrian is to overtake the former pedestrian whose speed is lower than approximately 70%of his own.The influence of the obstacle layout on pedestrian lane and egress efficiency is also studied with this model.The presence of a small obstacle does not obstruct the walking of pedestrians;in contrast,it may help to improve the egress efficiency by guiding the pedestrian flow and mitigating the reduction of pedestrian system orderliness.

ST-LaneNet: Lane Line Detection Method Based on Swin Transformer and LaneNet

The advancement of autonomous driving heavily relies on the ability to accurate lane lines detection.As deep learning and computer vision technologies evolve,a variety of deep learning-based methods for lane line detection have been proposed by researchers in the field.However,owing to the simple appearance of lane lines and the lack of distinctive features,it is easy for other objects with similar local appearances to interfere with the process of detecting lane lines.The precision of lane line detection is limited by the unpredictable quantity and diversity of lane lines.To address the aforementioned challenges,we propose a novel deep learning approach for lane line detection.This method leverages the Swin Transformer in conjunction with LaneNet(called ST-LaneNet).The experience results showed that the true positive detection rate can reach 97.53%for easy lanes and 96.83%for difficult lanes(such as scenes with severe occlusion and extreme lighting conditions),which can better accomplish the objective of detecting lane lines.In 1000 detection samples,the average detection accuracy can reach 97.83%,the average inference time per image can reach 17.8 ms,and the average number of frames per second can reach 64.8 Hz.The programming scripts and associated models for this project can be accessed openly at the following GitHub repository:https://github.com/Duane 711/Lane-line-detec tion-ST-LaneNet.

Stability-Considered Lane Keeping Control of Commercial Vehicles Based on Improved APF Algorithm

Regarding the lane keeping system,path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass,longer wheelbase and higher mass center.To improve the performance mentioned above comprehensively,the control strategy based on improved artificial potential field(APF)algorithm is proposed.In the paper,time to lane crossing(TLC)is introduced into the potential field function to enhance the accuracy of path tracking,meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source.The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory.In addition,adaptive inertial weight particle swarm optimization algorithm(AIWPSO)is applied to optimize the gain of each potential field function.The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably.Finally,the proposed control strategy is verified by the HiL test.It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving.

Monoamine Oxidase-B Inhibitor Rasagiline Effects on Motor and Non-Motor Symptoms in Individuals with Parkinson’s Disease: A Systematic Review and Meta-Analysis

Objective: In the manuscript titled Monoamine Oxidase-B Inhibitor Rasagiline Effects on Motor and Non-Motor Symptoms in Individuals with Parkinsons Disease: A Systematic Review and Meta-Analysis, the objective was to conduct a systematic review with meta-analysis to investigate the effects that Rasagiline has on motor and non-motor symptoms in individuals with PD. Introduction: Rasagiline is a second-generation monoamine oxidase-B (MAO-B) inhibitor used both as monotherapy and adjunctive therapy for Parkinsons Disease (PD). Methods: A systematic literature search and meta-analysis were performed with randomized control trials that investigated the effects of Rasagiline on motor and non-motor symptoms in individuals with PD. The systematic search was conducted in PubMed, Cochrane, and EBSCO databases. Methodological quality was assessed using the Cochrane Grading Recommendations Assessment, Development and Evaluation approach. Results: Fourteen studies were included in our review. There were trivial to small and statistically significant improvements in motor symptoms for individuals with PD treated with Rasagiline compared to placebo. Non-motor symptoms showed no significant improvement with Rasagiline compared to placebo in five of six meta-analyses. Results were based on very low to moderate certainty of evidence. Conclusion: 1 mg/day Rasagiline significantly improved Parkinsonian motor symptoms in individuals with PD compared with placebo. For all outcomes, the 1 mg/day Rasagiline group was favored over the placebo group.

An evolutionary game theory-based machine learning framework for predicting mandatory lane change decision

Mandatory lane change(MLC)is likely to cause traffic oscillations,which have a negative impact on traffic efficiency and safety.There is a rapid increase in research on mandatory lane change decision(MLCD)prediction,which can be categorized into physics-based models and machine-learning models.Both types of models have their advantages and disadvantages.To obtain a more advanced MLCD prediction method,this study proposes a hybrid architecture,which combines the Evolutionary Game Theory(EGT)based model(considering data efficient and interpretable)and the Machine Learning(ML)based model(considering high prediction accuracy)to model the mandatory lane change decision of multi-style drivers(i.e.EGTML framework).Therefore,EGT is utilized to introduce physical information,which can describe the progressive cooperative interactions between drivers and predict the decision-making of multi-style drivers.The generalization of the EGTML method is further validated using four machine learning models:ANN,RF,LightGBM,and XGBoost.The superiority of EGTML is demonstrated using real-world data(i.e.,Next Generation SIMulation,NGSIM).The results of sensitivity analysis show that the EGTML model outperforms the general ML model,especially when the data is sparse.

A Lane Change Model Considering the Stability of Cooperative Adaptive Cruise Control Platoon Fleet

In this article,lane change models for mixed traffic flow under cooperative adaptive cruise control(CACC)platoon formation are established.The analysis begins by examining the impact of lane changes on traffic flow stability.The influences of various factors such as lane change locations,timing,and the current traffic state on stability are discussed.In this analysis,it is assumed that the lane change location and the entry position in the adjacent lane have already been selected,without considering the specific intention behind the lane change.The speeds of the involved vehicles are adjusted based on an existing lane change model,and various conditions are analyzed for traffic flow disturbances,including duration,shock amplitude,and driving delays.Numerical calculations are provided to illustrate these effects.Additionally,traffic flow stability is factored into the lane change decision-making process.By incorporating disturbances to the fleet into the lane change income model,both a lane change intention model and a lane change execution model are constructed.These models are then compared with a model that does not account for stability,leading to the corresponding conclusions.

Modeling Bicycle Conflict on Non-Motorized Paths on Suburban College Campuses

Bicycling is an important way for college students and employees to get around campuses. With a rise in bicycling on campuses there comes a rise in bicycle collisions with pedestrians walking to and from classes and work. The literature review showed many papers involving bicycle conflict modeling but on roads with motor vehicles. While some aspects of this research can be applied to non-motorized paths, there is a lack of research strictly focusing on only bicycles and pedestrians. This study aims to fill this knowledge gap by developing a model to identify locations on roads and paths (hotspots) on college campuses that are likely to have a bicycle collision and predict the likelihood of a serious bicycle crash on a non-motorized path based on the characteristics of the path. This study identifies those interactions between bicyclists and pedestrians on non-motorized paths on a suburban college campus in Newark, USA. Findings suggest that pedestrian density of a path is a major factor in the maximum speed bicyclists can achieve. The wider the path is, the higher the maximum speed is that a bicyclist can obtain. This is because a wider path width decreases the pedestrian density. The grade of the path has little effect on bicycle speeds. The results of the models were displayed on a GIS map that is visually appealing to a viewer. The paths were color coded based on their level of safety, so it is easy to observe problematic areas of the network. This technique can be applied to the entire campus network of non-motorized paths to study the whole system. This can then be used by planners and designers to identify areas that need upgrading and improve the overall safety of the non-motorized path system.

Temporal Stability and Transferability of Non-Motorized and Total Trip Generation Models

Transportation systems provide a means for moving people and the goods from which they are spatially separated. Of the two means of surface transportation, the motorized mode is used extensively for utilitarian travel in developed countries. The increasing reliance on motorized travel has contributed to increased traffic congestion, air pollution, and greenhouse emissions. Non-motorized travel has recently received significant attention as a means to reduce congestion and environmental problems and improve human health. However, non-motorized modeling is generally underdeveloped. This study investigated some changes in non-motorized and total travel and the characteristics of the traveling public in 1990, 1995, 2001, and 2009 using a national travel survey. The study also investigated the temporal transferability of linear-regression trip generation models for non-motorized and total travel under such changes. High-income households made fewer non-motorized trips in 1990 and 1995 compared to 2001 and 2009. Persons aged 50 and over showed an increased demand for non-motorized travel, whereas children aged 0 - 15 showed a decreasing preference for non-motorized travel over time. Regarding temporal stability, only the coefficient for single-adult households with no children was stable across all of the analysis years. For both non-motorized and total travel, most model parameter estimates were stable short term but not long term. In general, the total travel models transferred better than non-motorized models, both short term and long term. Despite not finding universal stability in model parameter estimates, the models were marginally able to replicate travel in 2009 relative to the locally estimated 2009 model.

Connected and Autonomous Vehicles (CAVs) Challenges with Non-motorized Amenities Environments

With the deployment of Connected and Automated Vehicles in the coming decades,road transportation will experience a significant upheaval.CAVs(Connected and Autonomous Vehicles)have been a main emphasis of Transportation and the automotive sector,and the future of transportation system analysis is widely anticipated.The examination and future devel­opment of CAVs technology has been the subject of numerous researches.However,as three essential kinds of road users,pedestrians,bicyclists,and motorcyclists have experienced little to no handling.We explored the influ­ence of CAVs on non-motorized mobility in this article and seven various issues that CAVs face in the environment.

Redesign of Motorized and Non-motorized Transport in Cities and Sustainable Mobility

Redesign of motorized and non-motorized transport in cities and sustainable mobility possibilities and utilization of already occupied areas for motorized and non-motorized transport in cities. Reserved area for public transport may be the redesign much more to take advantage of without taking up new space in cities. Redesigned solutions shown in the work of public mass transport and the redesign of non-motorized transport, bicycle and pedestrian paths point to the improved use and safety of movement of passengers, cyclists and pedestrians. This paper presents five redesigned concept designs as improving existing forms of transport and movement of cyclists and pedestrians in cities. Redesigned conceptual designs of motorized and non-motorized transport in cities should serve as ideas for the growing problems of urban development in the segment of insufficient surface for pedestrians and cyclists, as well as sustainable mobility transport people in the city.

Enhancing Urban Mobility: Exploring the Potential of Exclusive Motorcycle Lane Using VISSIM

The proliferation of Mobility on Demand (MOD) services has ushered in a surge of ridesharing platforms, catalyzing the emergence of micro mobility solutions like motorcycle sharing. Consequently, motorcycles have witnessed unprecedented growth over recent decades. This proliferation, while offering convenience, has introduced challenges such as diminished road capacity, and compromised safety. This study advocates for the implementation of exclusive motorcycle lanes to mitigate the ensuing disorderliness using VISSIM microsimulation platform. Empirical data from a key corridor in Dhaka is harnessed to calibrate and simulate network performance scenarios—pre- and post-implementation of dedicated motorcycle lanes. The outcomes of our simulation experiments exhibit the implementation of dedicated motorcycle lanes leads to a reduction in vehicular throughput but improvement the flow of motorcycles. In addition, Surrogate Safety Measures (SSMs) demonstrate the safety improvements through implementation of the treatment.

Lane Line Detection Based on Improved PINet

Accurate perception of lane line information is one of the basic requirements of unmanned driving technology, which is related to the localization of the vehicle and the determination of the forward direction. In this paper, multi-level constraints are added to the lane line detection model PINet, which is used to improve the perception of lane lines. Predicted lane lines in the network are predicted to have real and imaginary attributes, which are used to enhance the perception of features around the lane lines, with pixel-level constraints on the lane lines;images are converted to bird’s-eye views, where the parallelism between lane lines is reconstructed, with lane line-level constraints on the predicted lane lines;and vanishing points are used to focus on the image hierarchy, with image-level constraints on the lane lines. The model proposed in this paper meets both accuracy (96.44%) and real-time (30 + FPS) requirements, has been tested on the highway on the ground, and has performed stably.

A Novel Ego Lanes Detection Method for Autonomous Vehicles

Autonomous vehicles are currently regarded as an interesting topic in the AI field.For such vehicles,the lane where they are traveling should be detected.Most lane detection methods identify the whole road area with all the lanes built on it.In addition to having a low accuracy rate and slow processing time,these methods require costly hardware and training datasets,and they fail under critical conditions.In this study,a novel detection algo-rithm for a lane where a car is currently traveling is proposed by combining simple traditional image processing with lightweight machine learning(ML)methods.First,a preparation phase removes all unwanted information to preserve the topographical representations of virtual edges within a one-pixel width around expected lanes.Then,a simple feature extraction phase obtains only the intersection point position and angle degree of each candidate edge.Subsequently,a proposed scheme that comprises consecutive lightweight ML models is applied to detect the correct lane by using the extracted features.This scheme is based on the density-based spatial clustering of applications with noise,random forest trees,a neural network,and rule-based methods.To increase accuracy and reduce processing time,each model supports the next one during detection.When a model detects a lane,the subsequent models are skipped.The models are trained on the Karlsruhe Institute of Technology and Toyota Technological Institute datasets.Results show that the proposed method is faster and achieves higher accuracy than state-of-the-art methods.This method is simple,can handle degradation conditions,and requires low-cost hardware and training datasets.

Traffic flow of connected and automated vehicles at lane drop on two-lane highway: An optimization-based control algorithm versus a heuristic rules-based algorithm

This paper investigates traffic flow of connected and automated vehicles at lane drop on two-lane highway. We evaluate and compare performance of an optimization-based control algorithm(OCA) with that of a heuristic rules-based algorithm(HRA). In the OCA, the average speed of each vehicle is maximized. In the HRA, virtual vehicle and restriction of the command acceleration caused by the virtual vehicle are introduced. It is found that(i) capacity under the HRA(denoted as C_(H)) is smaller than capacity under the OCA;(ii) the travel delay is always smaller under the OCA, but driving is always much more comfortable under the HRA;(iii) when the inflow rate is smaller than C_(H), the HRA outperforms the OCA with respect to the fuel consumption and the monetary cost;(iv) when the inflow rate is larger than C_(H), the HRA initially performs better with respect to the fuel consumption and the monetary cost, but the OCA would become better after certain time. The spatiotemporal pattern and speed profile of traffic flow are presented, which explains the reason underlying the different performance. The study is expected to help for better understanding of the two different types of algorithm.

Review and Analysis: Fate of Arsenic Applied to Canal Shipping Lane Vegetation and United States Military Base Grounds in the Panama Canal Zone

The opening of the Panama Canal in 1913 increased the availability of internationally traded goods and transformed ocean-shipping by shortening travel time between the Atlantic Ocean and Pacific Ocean. The canal sparked the growth of port authorities and increased ship tonnage on both coasts of Panama. Since the construction of the Panama Canal, in the 1910s, pesticides, herbicides and chemicals, including arsenic, have been essential for controlling wetland vegetation, including hyacinth, which blocked rivers, lakes, and the canal as well as managing mosquitoes. Pesticides and chemicals flowed into Lake Gatun (reservoir) either attached to sediment or in solution during the monsoon season. Lake Gatun was the drinking water source for most of the people living in the Panama Canal Zone. The United States military base commanders had the ability to order and use cacodylic acid (arsenic based) from the Naval Depot Supply Federal and Stock Catalog and the later Federal Supply Catalog on the military base grounds in the Panama Canal Zone. Cacodylic acid was shipped to Panama Canal Zone ports, including Balboa and Cristobal, and distributed to the military bases by rail or truck. The objective of this study is to determine the fate of arsenic: 1) applied between 1914 and 1935 to Panama Canal shipping lane hyacinth and other wetland vegetation and 2) cacodylic acid (arsenic) sprayed from 1948 to 1999 on the US military base grounds in the Panama Canal Zone.

网联混合流高速公路车道管理及通行能力分析

为提高网联混合交通流环境下高速公路基本路段通行能力,需针对车流量、网联自动驾驶车辆(CAV)比例制定与之匹配的车道管理策略。针对低CAV比例环境下CAV专用车道闲置浪费的问题,提出了一种CAV优先车道管理策略。利用跟驰模型推导了混合交通流基本图模型,构建了人工驾驶车辆(HV)专用车道、CAV专用车道、混行车道及CAV优先车道通行能力模型,分析了高速公路不同车道组合方案适用的交通需求范围。然后,以双向4车道高速公路为例,基于SUMO仿真平台分析了CAV优先车道性能,对不同车道组合方案的通行能力和适用范围进行了仿真验证,并从效率、安全及能耗角度对车道组合方案进行了比较。结果表明:在适用性方面,CAV比例p≤0.5时,两条混行车道适用的交通需求范围最大,CAV优先车道次之,CAV专用车道最小;在效率方面,p≤0.5时,设置CAV专用车道方案的效率最差,CAV优先车道表现较好,且p越小优先车道效率提升越显著;在安全、能耗方面,设置CAV专用车道方案的能耗和安全性能较佳,CAV优先车道次之;在p>0.5时,设置CAV专用车道方案的综合表现更优;在多种车道管理方案均可行时,相比于两条混行车道方案,采取其他车道管理措施在安全、能耗上的表现均有提升;在需求D≤2500 veh/h,p≤0.5时,相比于其他车道方案,采用CAV优先车道方案既能满足通行需求、降低能耗,也能更充分地利用道路资源,综合表现均衡。

基于数据驱动的快速路合流区加速车道长度的研究

设计长度合理的加速车道能有效地缓解快速路合流区频繁出现的交通瓶颈问题,因此采用数据驱动方法对快速路合流区的加速车道长度进行研究。利用无人机设备测取了快速路合流区的交通数据,从交通流特性及车辆汇入行为这两个角度对实测数据进行分析,得到了合流区车辆的驾驶行为;根据合流区交通流特点,对数据集进行聚类分析,使用生成对抗式网络训练不同合流区汇入行为车辆的跟驰换道模型,并与实测数据和SUMO仿真软件中内置模型进行对比分析;应用生成对抗式网络模型进行交通环境仿真,选取速度、交通密度、交通冲突率指标建立奖励评价函数,得出了加速车道长度设计的推荐值。研究结果表明:采用主线车辆提前减速和向内侧车道换道这两种手段,可实现协同换道避让匝道汇入的车辆;相比SUMO软件内置模型,生成对抗式网络模型更加贴近实际情况;仿真得出的单车道平行式加速车道长度分别在100、80、60 km/h情况下的推荐值为280、240、200 m。

基于驾驶场景与决策规则的智能汽车换道决策

复杂交通环境中,换道决策直接影响智能汽车自主换道效果,然而在换道决策过程中依旧存在着预测正确率低以及决策安全的问题。因此,针对这一问题,提出了基于驾驶场景和决策规则的换道决策模型。考虑换道后的交通行驶状况对换道决策的影响,引入换道后的期望速度和换道前后与前车的距离作为新的特征变量,基于特征变量与换道决策的相关性建立了换道决策规则。建立了模拟真实驾驶环境的换道场景数据集,扩充了NGSIM换道场景数据集,并对其进行了有效性验证。针对换道决策的多参数和非线性问题,提出了基于贝叶斯优化核函数的支持向量机模型,在换道场景数据集上进行测试验证。结果表明:新引入的决策特征变量对换道行为有积极作用,换道场景数据集能够模拟真实的换道场景,可进一步应用到换道决策和轨迹规划的研究中,支持向量机模型对换道行为的预测正确率达95.40%,高于其他机器学习分类器,提高了换道行为的安全性。

基于时域卷积网络与注意力机制的车辆换道轨迹预测模型

精准的车辆轨迹预测模型可以为自动驾驶车辆提供其周围车辆的准确运动状态信息,进而判断本车与周围车辆短期内是否有发生冲突的可能性。本文提出一种基于时域卷积网络与注意力机制(Temporal Convolutional Networks with Attention mechanism,TCN-Attention)的车辆换道轨迹预测模型。该模型以时域卷积网络作为当前输入的特征提取器,利用时间与空间注意力机制使模型在不同时间和空间位置之间建立动态关联,更准确地捕捉车辆之间的动态时空相关性,实现准确预测车辆换道轨迹。与传统单一车辆轨迹特征输入不同,本文通过对输入特征进行多维扩充与融合,进一步提高了轨迹预测准确率。此外,本文提出一种换道执行起止时刻定义方法更准确地确定数据集中的换道起止时刻。实验表明,本文所提模型能以高准确率预测变换车道轨迹,在整体效果上优于其他深度学习模型,与ConvLSTM (Convolution Long Short-Term Memory)相比,TCN-Attention的平均绝对误差(Mean Absolute Error,E_(MAE))降低了69.8%,均方根误差(Root Mean Square Error,E_(RMSE))降低了49.15%,平均绝对百分比误差(Mean Absolute Percentage Error,E_(MAPE))降低了14.24%。