城市轨道交通线网客流预测及特征研究

建设高效绿色的轨道交通系统已成为各大城市提升城市形象、缓解交通拥堵、方便居民出行的首选,而客流预测是其中必不可少的重要工作。本文结合普洱市城市轨道交通客流预测实例,在研究现状城市居民出行特征、公交发展水平及道路系统运行特征基础上,利用四阶段模型理论,分析普洱市不同组团间的居民出行量、出行分布、出行结构等特征,对普洱市城市轨道交通线网客流进行测试,通过预测线路的断面流量、客运量、负荷强度,确定不同的比选方案指标,为城市轨道交通线网规划提供量化支撑。     

城市轨道交通客流强度及其基本特征分析

本文结合国内外部分城市轨道交通客流强度数据,分析了不同城市轨道交通线网以及不同类型轨道交通线路客流强度的基本特征,从线路空间与网络演变角度剖析了线路客流强度演变的内涵,提出了改善城市轨道交通客流预测工作的建议。     

轨道交通建设前期工作对工程造价的影响分析

结合部分城市轨道交通前期工作的实际,介绍了轨道交通建设中线网规划、线路沿线土地利用规划的制定、客流预测以及线路敷设方式的确定等前期工作对地铁造价不同的影响方式和影响程度,提出了降低前期投资的几个方法和建议。     

上海市域铁路车站换乘客流组织研究——以机场联络线为例

上海市域铁路网包括新建市域铁路和利用既有铁路改建的市域铁路，根据上海市域铁路与城市轨道交通、都市圈城际铁路的融合发展情况，从线网规划、客流特征、票务系统等方面梳理多层次轨道交通方式间换乘的影响因素，并以上海市域铁路为主体，分析其与其他轨道交通方式间换乘的特点及方式。最后以机场联络线为例，总结机场联络线各站换乘情况，并根据不同类型的客流特点提出相应的客流组织方案。     

西安城市轨道交通环线路由及换乘分析

随着西安地铁3号线的正式运营,西安城市轨道交通即将迎来网络式发展时代。轨道交通环线是城市轨道交通线网中的一种线型,对于方格型路网城市有举足轻重的作用。本文梳理西安轨道交通8号线环线的相关规划,在分析8号线沿线功能区的基础上,探讨其路由与路径特征并依据客流预测的结果分析各换乘站换乘情况,进一步验证西安地铁8号线对西安市发展的重要作用。     

城市轨道交通环线规划及案例分析

在城市轨道交通线网规划实践中,对于中心城区面积较大、各方向规划较均衡的城市,在规划放射线达到一定数量时需研究规划环线的必要性。分析了城市轨道交通环线的概念、功能定位、规划条件、运营组织;以宁波轨道交通5号线为例,从线网形态、城市布局、线路功能、城市发展、客流及运营组织等角度,详细论述了新一轮规划中将该线由U形线改为环线的可行性及必要性。     

基于圈层的轨道交通一体化客流预测方法：以粤港澳大湾区为视角

为推动粤港澳大湾区轨道交通一体化的持续发展，从区域轨道客流预测的复杂性作为切入点，在阐述粤港澳大湾区多层次轨道交通系统发展概况的基础上，进行湾区交通圈层划分，并论述了不同圈层出行特征的差异及联系；通过现有城市轨道交通和铁路客流预测方法的回顾及适应性分析，肯定了“四阶段法”类预测方法在轨道交通规划设计阶段应用的优越性；进而提出圈层分级的轨道交通预测思路，并以四阶段法为核心，搭建粤港澳大湾区尺度下的轨道交通一体化客流预测框架。     

城市快速轨道交通建设规划编制要点及案例分析

以《福州市城市快速轨道交通建设规划》的编制为实例，阐述了建设规划编制应加强必要性及紧迫性的分析论述、线网建设时序及近期建设规模研究、客流预测、近期工程方案、资金筹集与平衡等工作内容，建议加强近期建设项目的外部建设条件分析和可行性研究，对近期项目建成后的效果进行了评价。     

徐家汇轨道交通枢纽建设配套交通规划初探

以徐家汇功能定位为出发点,通过对交通现状的分析,结合轨道交通枢纽的规划布局,预测由轨道交通建设带来的区域交通及客流的流量、走向的变化趋势;结合徐汇区的区域交通规划,提出若干建议。     

郑州都市区轨道交通线网规划初探

目前郑州都市区轨道交通线网规划处于起步阶段,本文探讨了轨道交通线网规划原则、规划方案等问题,以期为后续建设提供参考。     

Path flow estimator for planning applications in small communities

This paper presents an alternative planning framework to model and forecast network traffic for planning applications in small communities, where limited resources debilitate the development and applications of the conventional four-step travel demand forecasting model. The core idea is to use the Path Flow Estimator (PFE) to estimate current and forecast future traffic demand while taking into account of various field and planning data as modeling constraints. Specifically, two versions of PFE are developed: a base year PFE for estimating the current network traffic conditions using field data and planning data, if available, and a future year PFE for predicting future network traffic conditions using forecast planning data and the estimated base year origin–destination trip table as constraints. In the absence of travel survey data, the proposed method uses similar data (traffic counts and land use data) as a four-step model for model development and calibration. Since the Institute of Transportation Engineers (ITE) trip generation rates and Highway Capacity Manual (HCM) are both utilized in the modeling process, the analysis scope and results are consistent with those of common traffic impact studies and other short-range, localized transportation improvement programs. Solution algorithms are also developed to solve the two PFE models and integrated into a GIS-based software called Visual PFE. For proof of concept, two case studies in northern California are performed to demonstrate how the tool can be used in practice. The first case study is a small community of St. Helena, where the city’s planning department has neither an existing travel demand model nor the budget for developing a full four-step model. The second case study is in the city of Eureka, where there is a four-step model developed for the Humboldt County that can be used for comparison. The results show that the proposed approach is applicable for small communities with limited resources.     

Traffic assignment by trip type using volume restraint and link restraint for application in small urban areas

This research involved the development of a new traffic assignment model consisting of a set of procedures for an urbanized area with a population of 172,000. Historical, social, and economic data were used as input to conventional trip generation and trip distribution models to produce a trip table for network assignment. This fixed table was divided into three trip types: external-external trips, external-internal trips, and internal-internal trips. The methodology used to develop the new traffic assignment model assigned each of the trip types by varying the diversion of trips from the minimum path. External-external trips were assigned on a minimum path routing and external-internal trips were assigned with a slight diversion from the minimum path. Internal-internal trips were assigned with more diversion than external-internal trips and adjusted by utilizing iterative volume restraint and incremental link restraint. A statistical analysis indicated that assigning trips by trip types using trip diversion and volume and link restraint produces a significant improvement in the accuracy of the assigned traffic volumes.     

基于贝叶斯网络模型的交通状态预测

城市的交通状态是可以预测的。有效的交通状态预测能优化交通状态,减少交通阻塞。贝叶斯网络(Bayesian Networks,BN)是目前不确定知识和推理领域最有效的理论模型之一。文章在综合考虑交通阻塞成因的基础上构建网络模型,在已有的交通状态数据的基础上提出基于贝叶斯法则的学习算法,并通过计算变量间的条件概率来计算交通阻塞发生的可能性,达到预测的目的。     

Forecasting high-speed rail ridership using a simultaneous modeling approach

Abstract

The newly launched, June 2009, US High-Speed Intercity Passenger Rail Program has rekindled a renewed interest in forecasting high-speed rail (HSR) ridership. The first step to the concerted effort by the federal, state, rail, and other related agencies to develop a nationwide HSR network is the development of credible approaches to forecast the ridership. This article presents a nested logit/simultaneous choice model to improve the demand forecast in the context of intercity travel. In addition to incorporating the interrelationship between trip generation and mode choice decisions, the simultaneous model also provides a platform for the same utility function flowing between both the decision-making processes. Using American Travel Survey data, supplemented by various mode parameters, the proposed model improves the forecast accuracy and confirms the significant impact of travel costs on both mode choice and trip generation. Furthermore, the cross elasticity of mode choice and trip generation related to travel costs and other modal characteristics may shed some light on transportation policies in the area of intercity travel, especially in anticipation of HSR development.     

城市占道施工区交通流分配研究

文章阐述了占道施工区交通流分配的概念及相关措施,采用改进的LOGIT多路径概率模型作为占道施工区路网交通流分配模型,确定路网的有效出行路径及合理的交通分配量,并以广州市天河区黄埔大道占道施工为例,运用该模型对其周围路网交通流分配量进行分析,所得结果较实际地反映了交通状况,证明该模型算法是科学合理的。     

Maximum car ownership under constraints of road capacity and parking space

This paper presents a model for determining the maximum number of cars by zones in view of the capacity of the road network and the number of parking spaces available. In other words, the proposed model is to examine whether existing road network and parking supply is capable of accommodating future zonal car ownership growth (or the reserve capacity in each zone); i.e. the potential maximum zonal car ownership growth that generates the road traffic within the network capacity and parking space constraints. In the proposed model, the vehicular trip production and attraction are dependent on the car ownership, available parking spaces and the accessibility measures by traffic zones. The model is formulated as a bi-level programming problem. The lower-level problem is an equilibrium trip distribution/assignment problem, while the upper-level problem is to maximize the sum of zonal car ownership by considering travellers’ route and destination choice behaviour and satisfying the network capacity and parking space constraints. A sensitivity analysis based heuristic algorithm is developed to solve the proposed bi-level car ownership problem and is illustrated with a numerical example.     

Travel time estimation for urban road networks using low frequency probe vehicle data

The paper presents a statistical model for urban road network travel time estimation using vehicle trajectories obtained from low frequency GPS probes as observations, where the vehicles typically cover multiple network links between reports. The network model separates trip travel times into link travel times and intersection delays and allows correlation between travel times on different network links based on a spatial moving average (SMA) structure. The observation model presents a way to estimate the parameters of the network model, including the correlation structure, through low frequency sampling of vehicle traces. Link-specific effects are combined with link attributes (speed limit, functional class, etc.) and trip conditions (day of week, season, weather, etc.) as explanatory variables. The approach captures the underlying factors behind spatial and temporal variations in speeds, which is useful for traffic management, planning and forecasting. The model is estimated using maximum likelihood. The model is applied in a case study for the network of Stockholm, Sweden. Link attributes and trip conditions (including recent snowfall) have significant effects on travel times and there is significant positive correlation between segments. The case study highlights the potential of using sparse probe vehicle data for monitoring the performance of the urban transport system.     

Simplified transport models based on traffic counts

Having accepted the need for the development of simpler and less cumbersome transport demand models, the paper concentrates on one possible line for simplification: estimation of trip matrices from link volume counts. Traffic counts are particularly attractive as a data basis for modelling because of their availability, low cost and nondisruptive character. It is first established that in normal conditions it may be possible to find more than one trip matrix which, when loaded onto a network, reproduces the observed link volumes. The paper then identifies three approaches to reduce this underspecification problem and produce a unique trip matrix consistent with the counts. The first approach consists of assuming that trip-making behaviour can be explained by a gravity model whose parameters can be calibrated from the traffic counts. Several forms of this gravity model have been put forward and they are discussed in Section 3. The second approach uses mathematical programming techniques associated to equilibrium assignment problems to estimate a trip matrix in congested areas. This method can also be supplemented by a special distribution model developed for small areas. The third approach relies on entropy and information theory considerations to estimate the most likely trip matrix consistent with the observed flows. A particular feature of this group is that they can include prior, perhaps outdated, information about the matrix.These three approaches are then compared and their likely areas for application identified. Problems for further research are discussed and finally an assessment is made of the possible role of these models vis-a-vis recent developments in transport planning.     

Path-constrained traffic assignment: A trip chain analysis under range anxiety

This paper proposes and analyzes a distance-constrained traffic assignment problem with trip chains embedded in equilibrium network flows. The purpose of studying this problem is to develop an appropriate modeling tool for characterizing traffic flow patterns in emerging transportation networks that serve a massive adoption of plug-in electric vehicles. This need arises from the facts that electric vehicles suffer from the “range anxiety” issue caused by the unavailability or insufficiency of public electricity-charging infrastructures and the far-below-expectation battery capacity. It is suggested that if range anxiety makes any impact on travel behaviors, it more likely occurs on the trip chain level rather than the trip level, where a trip chain here is defined as a series of trips between two possible charging opportunities (Tamor et al., 2013). The focus of this paper is thus given to the development of the modeling and solution methods for the proposed traffic assignment problem. In this modeling paradigm, given that trip chains are the basic modeling unit for individual decision making, any traveler’s combined travel route and activity location choices under the distance limit results in a distance-constrained, node-sequenced shortest path problem. A cascading labeling algorithm is developed for this shortest path problem and embedded into a linear approximation framework for equilibrium network solutions. The numerical result derived from an illustrative example clearly shows the mechanism and magnitude of the distance limit and trip chain settings in reshaping network flows from the simple case characterized merely by user equilibrium.     

A note on trip matrix estimation from link traffic count data

In this note, a simple network equilibrium based approach for estimating a trip matrix using link traffic count data is proposed. In essence a combined distribution and assignment model is formulated in which the link flow data serve to furnish an estimate for the sum of the integrals of the link cost functions. A comparison of this model with others proposed previously is made.