城市交通网络中电动巴士的定位

一、城市交通网络中电动巴士的发展动因 随着各类交通设施的规模逐步扩大,各种城市交通方式之间的关联性逐步增强,并越来越依赖于城市交通网络整体效益的发挥.随着城市居民出行距离逐步增长,出行方式趋于多样化,越来越需要以多种方式组合的形式来完成出行的需要.     

信息技术对城市客运交通结构的影响分析

定性分析了信息技术对城市客运量的替代作用,指出信息技术的发展使得部分城市交通虚拟化,城市居民出行目的结构随之而改变,进而导致了城市交通客运结构的变化.并构建了信息技术对客运交通结构影响的模型,为信息时代的交通规划工作提供依据.     

城市居民出行方式选择行为及影响因素分析

居民出行行为研究在交通管理和控制中有着非常重要的作用。对城市居民个体出行行为的研究,是城市交通规划、建设、管理中一项不可缺少的基础性工作。居民的出行方式选择通过改变城市交通客运结构来影响城市交通系统的运行情况。笔者基于对城市居民出行可供选择的交通方式优缺点的分析,对居民出行方式选择的过     

关于构建三层次公交网络的研究

改革开放以来,随着经济和社会的不断发展,我国大中城市居民生活水平有了大幅度提升,城市居民出行需求和机动化出行强度与日俱增.城市交通面临着小汽车保有量高速增长,机动车规模日益扩大,道路容量不足,交通管理技术水平低以及公共交通发展缓慢等问题,交通拥堵严重影响了城市居民的出行.因此,大力发展城市公共交通成为缓解城市交通供需矛盾、解决居民出行问题的一项基本政策.要提高城市公交系统的吸引力和竞争力,就需要针对现状公交网络存在的问题,对其进行优化和调整,构建一种适应居民出行需求的新公交网络体系.笔者通过对佛山市南海区公交三级线网规划及近期实施方案的研究,就构建三层次公交线网进行相关探讨,希望能为同等城市公交网络优化提供参考.     

言息技术对城市客运交通结构的影响分析

定性分析了信息技术对城市客运量的替代作用，指出信息技术的发展使得部分城市交通虚拟化，城市居民出行目的结构随之而改变，进而导致了城市交通客运结构的变化。并构建了信息技术对客运交通结构影响的模型，为信息时代的交通规划工作提供依据。     

城市公共交通分担率影响因素及提高措施

一、公共交通分担率 公共交通分担率简称公交分担率,是指城市居民出行方式中选择公共交通(包括常规公交和轨道交通)的出行量占总出行量的比率,是衡量公共交通发展、城市交通结构合理性的重要指标.城市的公交分担率水平对于政府和行业部门了解公共交通的服务状况、制定公交优先相关政策、优化调整城市交通结构、宏观指导公共交通的发展,具有重要的理论意义和实际应用价值.     

珠海:双轴协同作战激发信号管控新活力

城市道路资源供给在有限时空内是相对稳定的,当交通出行需求量超出道路资源供给能力时会导致交通供需失衡,交通出行"存量博弈"就在城市日常机动化、多样化交通出行需求之间发生了,从而导致城市交通拥堵等问题出现,这是城市交通拥堵产生底层的逻辑.为满足群众出行需求,最大限度预防和化解城市交通拥堵,减少交通事故,广东珠海交警先后在技...     

提高济南市快速公交运营速度方法探讨

快速公交系统(bus rapid transit,BRT)是以城市发展轨道交通的过渡阶段而出现的.由于在城市扩张的初期,城市结构及经济区域布局的变化主要表现在空间利用和平面扩展上,致使流动人口大为增加,居民出行频率更为频繁,城市交通的拥堵问题也就日趋紧张.     

后疫情时代下武汉市城市交通供求分析

城市交通是城市道路(地面、地下、高架、水道、索道等)系统间的公众出行和客货输送的集合,城市交通的重点是客运.随着社会经济的不断发展,交通拥堵、汽车尾气污染等城市交通问题日益加剧,政府正在大力倡导优先发展城市公共交通.城市公共交通是专为城市地区居民出行活动的需要而提供的营业性客运交通,最常见的城市公共交通有公交、地铁、出租车、共享单车等.相互诱导、共同提高是城市公共交通供给和需求之间存在的一大特征.以地铁为例,当城镇化不断扩大时,城市居民出行需求也会不断增大,随之而来的是政府不断增加地铁投资,提高公共交通供给量.     

城市客运交通系统结构研究

在解析城市客运交通系统合理结构的基础上,根据居民出行方式选择随距离和综合交通条件而变化的内在规律,以居民出行距离分布特征为基础,论证常规公交与城市轨道交通出行全过程广义效用值边际效应,综合构建城市客运交通方式出行选择模型。以某中心城市为例,计算结果表明:以出行距离分布特征为基础,考虑城市综合交通条件,结合未来客运结构辨识而建立的城市出行方式选择模型具有较好的拟合特征和适用性。     

Willingness to spend and road pricing rates

Using a theoretical model of urban transport system the paper examines the influence of distribution of willingness to spend within the urban population on road pricing rates. It shows that the rates that must be imposed in an urban area in order to maintain pollutant concentration and congestion due to traffic within acceptable levels is heavily dependent on the distribution of the urban population’s willingness to spend. This fact severely limits the reliability of any method for calculating road pricing rates based on theoretical analysis, so that an experimental approach seems necessary. The paper shows that a relation exists between the toll rate per kilometer of trip and the average traffic congestion, which is typical of each urban area and can be determined experimentally by successively imposing three different rates and measuring the corresponding congestion levels. The relation can then be used to determine the pricing scheme when the purpose of road pricing is to maintain both the congestion and the environmental effects due to urban traffic below acceptable thresholds. An example shows how the model can help policymakers in decision-making processes.     

A multilayer model to simulate cruising for parking in urban areas

In this paper we propose an assignment model on urban networks to simulate parking choices; this model is able to simulate the impact of cruising for parking on traffic congestion. For simulating parking choice and estimating the impact of cruising on road congestion we propose a multi-layer network supply model, where each layer simulates a trip phase (on-car trip between the origin and destination zone, cruising for parking at destination zone and walking egress trip). In this model the cruising time is explicitly simulated on the network. The proposed model is tested on a trial network and on a real-scale network; numerical tests highlighted that the proposed model is able to simulate user parking choice behaviour and the impact of cruising for parking upon road congestion, particularly when the average parking saturation degrees exceed 0.7.     

Network structure and travel patterns: explaining the geographical disparities of road network vulnerability

Inevitably, links in the road network are sometimes disrupted because of adverse weather, technical failures or major accidents. Link closures may have different economic and societal consequences depending on in which regions they occur (regional importance), and users may be affected differently depending on where they travel (regional exposure). In this paper we investigate in what way these geographical disparities depend on the road network structure and travel patterns. We propose aggregate supply-side (link redundancy, network scale, road density, population density) and demand-side (user travel time, traffic load) indicators and combine them in statistical regression models. Using the Swedish road network as a case study, we find that regional importance is largely determined by the network structure and the average traffic load in the region, whereas regional exposure is largely determined by the network structure and the average user travel time. Our findings show that the long-term vulnerability disparities stem from fundamental properties of the transport system and the population densities. Quantitatively, they show how vulnerability depends on different variables, which is of interest for robust network design.     

Investigating the associations between road network structure and non-motorist accidents

Road networks channel traffic flow and can impact the volume and proximity of walking and bicycling. Therefore, the structure of road networks—the pattern by which roads are connected—can affect the safety of non-motorized road users. To understand the impact of roads’ structural features on pedestrian and bicyclist safety, this study analyzes the associations between road network structure and non-motorist-involved crashes using data from 321 census tracts in Alameda County, California. Average geodesic distance, network betweenness centrality, and an overall clustering coefficient were calculated to quantify the structure of road networks. Three statistical models were developed using the geographically weighted regression (GWR) technique for the three structural factors, in addition to other zonal factors including traffic behavior, land use, transportation facility, and demographic features. The results indicate that longer average geodesic distance, higher network betweenness centrality, and a larger overall clustering coefficient were related to fewer non-motorist-involved accidents. Thus, results suggest that: (1) if a network is more highly centered on major roads, there will be fewer non-motorist-involved crashes; (2) a network with a greater average number of intersections on the shortest path connecting each pair of roads tends to experience fewer crashes involving pedestrians and bicyclists; and (3) the more clustered road networks are into several sub-core networks, the lower the non-motorist crash count. The three structural measurements can reflect the configuration of a network so that it can be used in other network analyses. More information about the types of road network structures that are conducive to non-motorist traffic safety can help to guide the design of new networks and the retrofitting of existing networks. The estimation results of GWR models explain the spatial heterogeneity of correlations between explanatory factors and non-motorist crashes, which can support regional agencies in establishing local safety policies.     

山地城市高铁站房综合交通规划和竖向设计——以贵阳北站综合交通枢纽规划为例

结合贵阳北站综合交通枢纽规划设计实践,针对山地城市地形地貌复杂、交通设计技术条件高等特点,通过构建基于复杂地形的研究框架,整合区域资源,探讨站房交通枢纽、城市道路交通、地下空间利用三者间的设计难点,提出相应的解决方法和设计对策。     

小交通量路网改造资金优化投资模型的研究

传统的以经济效益为目标函数的优化模型在一定程度上影响了路网改造项目决策的合理性.针对我国西部地区经济落后、交通量小、客货在途时间价值低的特性,采用以道路服务水平提高值作为目标函数,资金投入作为约束条件的优化模型,对西部路网改造资金进行了优化分配.该模型在湖北省恩施地区的路网改造资金优化中取得良好效果.     

Modeling different penetration rates of eco-driving in urban areas: Impacts on traffic flow and emissions

Climate change and air quality are two main environmental challenges in metropolitan areas. As road transportation is one of the main contributors, public administrations are facing these problems with a number of complementary policy measures: shift to cleaner modes, new fuels and vehicle technologies, demand management, and the use of information and communication technologies (ICT) applied to transportation. Eco-driving is one of the measures that present large fuel savings at individual level. Although these savings are well documented in the literature, few studies focus on how eco-drivers driving patterns affect the surrounding vehicles and the traffic in general, and more particularly what would be the impact when the number of eco-drivers grows. Using a traffic microsimulation tool, four models in urban context have been built, corresponding to the different types of urban roads. Both the base-case and the parameters setting to simulate eco-driving have been calibrated with real data collected through floating vehicles performing the trips with normal and eco behaviors. In total, 72 scenarios were simulated, varying the type of road, traffic demand, and the percentage of eco-drivers. Then, the CO₂ and NO_x emissions have been estimated through a microscopic emission model. The results show that in scenarios with low or medium demand levels and increasing number of eco-drivers, the effects are positive in terms of emissions. On the other side, with high percentage of eco-drivers and high traffic demand, the emissions rise. Higher headways and smooth acceleration and decelerations increase congestion, producing higher emissions globally.     

Planning for an unpredictable future: Transport in Great Britain in 2030

This paper describes a research study, which explores alternative future scenarios for Great Britain in the year 2030 and the implications these have for travel demand and transport provision. Five alternative future scenarios are represented in the GB national transport model and forecasts are obtained for trip making, traffic levels, congestion and emissions in 2030. For all scenarios it is expected that there will be significant traffic growth. Traffic growth is restricted most in scenarios including distance-based road charging on motorways and trunk roads. However, congestion and carbon dioxide emissions are most effectively limited in scenarios with congestion-based road charging, major improvements to urban public transport and investment in new fuel technologies and in improving engine efficiency.     

Exploring the spatial impacts of human activities on urban traffic crashes using multi-source big data

Traffic crashes are geographical events, and their spatial patterns are strongly linked to the regional characteristics of road network, sociodemography, and human activities. Different human activities may have different impacts on traffic exposures, traffic conflicts and speeds in different transportation geographic areas, and accordingly generate different traffic safety outcomes. Most previous researches have concentrated on exploring the impacts of various road network attributes and sociodemographic characteristics on crash occurrence. However, the spatial impacts of human activities on traffic crashes are unclear. To fill this gap, this study attempts to investigate how human activities contribute to the spatial pattern of the traffic crashes in urban areas by leveraging multi-source big data. Three kinds of big data sources are used to collect human activities from the New York City. Then, all the collected data are aggregated into regional level (ZIP Code Tabulation Areas). Geographically Weighted Poisson Regression (GWPR) method is applied to identify the relationship between various influencing factors and regional crash frequency. The results reveal that human activity variables from multi-source big data significantly affect the spatial pattern of traffic crashes, which may bring new insights for roadway safety analyses. Comparative analyses are further performed for comparing the GWPR models which consider human activity variables from different big data sources. The results of comparative analyses suggest that multiple big data sources could complement with each other in the coverage of spatial areas and user groups, thereby improving the performance of zone-level crash models and fully unveiling the spatial impacts of human activities on traffic crashes in urban areas. The results of this study could help transportation authorities better identify high-risky regions and develop proactive countermeasures to effectively reduce crashes in these regions.     

Correlation analysis of day-to-day origin-destination flows and traffic volumes in urban networks

The trip patterns on an urban network can be represented by two main variables: origin-destination flows (OD flows), defined as the number of trips between two locations over a given time period, and traffic volumes, defined as the number of vehicles that cross a street over a given time interval. Past research on the dynamic of traffic assignment and OD estimation suggested that the traveler's decisions vary on a day-to-day basis and that their most recent decisions may affect their current travel decisions. Based on these assumptions, this study analyzed the autocorrelation of a set of day-to-day series of traffic volumes and OD flows generated from a large collection of traffic sensors, identifying the data's correlation structure over different locations and OD pairs in an urban network. To this end, a method for data treatment of the 2017 dataset from the traffic monitoring system of Fortaleza, Brazil, was employed, which consisted in the following major steps: data cleaning due to equipment failure, definition of traffic profiles for typical and atypical months, definition of daily traffic periods, selection of suitable devices to obtain OD flows, and detection of outliers in the time series. The traffic profiles and the daily traffic periods were defined by applying clustering techniques. The analysis of autocorrelation was performed after controlling for seasonal effects in the data by applying regression analysis. This study contributes to understand how the dynamic of trip patterns varies over space due to the spatial distribution of the city's activities and the network's spatial centrality. The analysis of 144 sets of traffic volumes throughout 2017 suggests that the autocorrelation of traffic volumes should be higher in congested central areas where multiple options of route are available. It seems that, for large congested networks, which present many uncertain factors (e.g., accidents, variable weather, multiple paths, etc.), part of the users do not have complete knowledge of the network's performance, and must rely on experience and habit to decide their routes, especially at more centralized locations of the network. The analysis of serial correlation in the series of sample OD flows between regions showed that the city's central area, where more commercial and service-related activities take place, seems to influence the dynamic of OD flows, probably due to the occurrence of more non-commuting trips to the central area of the city.