森林公园慢行交通系统规划分析

文章基于森林公园慢行交通系统的构成及特点,从宏观、中观、微观三个层面分析了森林公园慢行交通系统的研究框架,并以鹿寨香桥岩森林公园为例,探讨了森林公园慢行交通系统的规划与构建内容。     

面向活力提升的城市郊区大学校园慢行交通改善规划设计——以同济大学嘉定校区为例

受城市中心区用地限制,近年来各大学因自身发展需要纷纷在城市郊区兴建校区。然而,现有城市郊区大学校园普遍存在空间尺度过大、人车冲突隐患严重、校园缺乏活力等问题。慢行是大学校园内最主要的出行方式,对慢行系统的改善规划设计成为提升校园活力的有效手段。通过分析厦门、上海、昆明和天津等城市的慢行交通系统规划,对比国内外高校的校园规划实例,研究校园慢行规划的特殊性。以同济大学嘉定校区为例,基于公共空间—公共生活调查方法对慢行交通现状进行调查分析,提出安全、便捷、舒适、活力的规划目标;将慢行交通设施与公共空间改造相结合,提出步行和自行车改善策略和方案,并对重点片区进行改善设计。     

成都市慢行交通现状调查分析与研究

作为城市交通系统必不可少的一部分,慢行交通以其绿色环保、便捷经济等优点越来越受到人们的关注。为了探究成都市的慢行交通现状,在成都市中心城区划分出八大功能分区,针对不同慢行方式使用者进行相应的问卷调查,同时在不同功能分区对其街道慢行交通设施进行实地勘测。通过调查,取得4908份慢行使用者数据及问卷结果,248处街道的慢行交通设施现状调查数据表。通过对调查数据的统计与分析,得出了成都市慢行交通现状,找出了存在的问题和具有的优势,并结合成都市实际情况,提出慢行交通系统改善措施及发展建议。     

可持续发展综合交通运输系统规划理论研究

阐述了可持续发展综合交通运输系统的内涵,从运输供给、需求和运输方式的外部性三个方面指出了可持续发展综合交通运输系统的三个发展方向;对发达资本主义国家综合交通体系形成与发展进行了具体的分析,提出了其发展的三个阶段论即发展阶段、调整阶段和可持续发展阶段;论文对国内外综合交通体系的发展现状及规划理论研究现状进行了整体分析,进而从宏观、中观、微观、操作四个层面提出建立可持续发展综合交通运输系统规划理论体系的具体研究任务。     

可持续理念下绿色交通规划方法研究

交通规划对于经济社会发展具有重要的支撑引导作用。传统交通规划方法强调需求对于规划方案的决定关系,容易导致不同运输方式的自发发展,对综合交通系统顶层引导作用不足。本文在探讨交通需求与交通供给的互动关系的基础上,提出了绿色交通规划方法的一般流程和生态评价方法。     

基于一卡通数据的公共自行车慢行系统构建

随着城市的发展,道路交通资源日益紧缺,伴随的环境问题越来越突出,为缓解交通出行紧张压力,满足广大公众出行内需,慢行系统正越来越备受人们的关注与青睐,目前十分流行的共享单车以及传统的公共自行车正是解决城市居民短距离出行的最佳方式。基于以上的问题及出行需求,本文将分析建设城市慢行系统的背景及意义,并初步提出了基于一卡通的公共自行车慢行系统构建模型及其应用模式。     

城市充电基础设施规划的思考

城市交通是实现碳达峰、碳中和的重要领域，电动汽车是节能减排的重要抓手，优化充电基础设施布局是当务之急。本文针对当前充电基础设施发展存在的问题，提出城市充电基础设施的规划方法，宏观层面分析城市组团功能确定建设优先级，中观层面提出“点—线—面”结合的规划方法和合理的服务半径，建设充电基础设施网络，微观层面以工作、居住、游憩、交通四大基本城市活动，满足不同场景充电需求。最后提出落实用地保障、建设时序等保障措施。     

慢行交通系统实践与设施规划研究

我国正处于机动化发展的快速时期,机动车保有量保持逐年递增趋势,交通拥堵、交通事故及严重违反交通安全的行为不断上升,慢行交通系统的研究正在逐渐受到公众的关注。文章对慢行交通系统组成及各自特点进行阐述,研究了慢行交通设施的规划设置方法,并提出了具体设计方案,为实现城市低碳交通及城市交通可持续发展提供新思路。     

东京轨道交通发展对运营组织规划的启示

国内很多城市的轨道交通存在规划、建设和运营阶段相互剥离的问题，由此导致线网规模加速扩张无法满足多样化客流出行需求。相较而言，东京在处理轨道交通发展与运营组织的关系方面的先进经验值得借鉴，本文以东京都市圈为研究对象，探究了东京轨道交通发展与运营组织规划的关系。首先，从线路建设和运营组织两个方面剖析轨道交通与城市功能分区之间的互动关系。其次，以东武东上线+西武池袋线廊道为例，从线路建设时序与城市发展时序的角度研究东京轨道交通线路建设的基本特征，阐释城市的功能分区、空间布局与轨道交通相互协调发展的过程。进一步分析了东京轨道交通运营组织的基本特征，从运行能力、运行交路和停站方案三个角度进行研究，研究结果表明城市轨道交通可通过多样化的运营组织模式引导乘客的出行。最后，结合东京轨道交通发展的先进经验，针对我国线网规模庞大、发展时间较早的城市，提出应在宏观层面统筹好运力布局，在中观层面兼顾运营交路和职住关系，在微观层面优化运营技术手段、提高运营效率的发展策略。     

共享单车停放区设计规划——以南码头路街道为例

随着共享单车的兴起,共享单车在居民出行"最后一千米"承担着愈来愈重要的角色。共享单车作为城市慢行交通的重要组成部分,其带来的乱停、乱放和机非混行干扰交通流等问题应引起城市交通管理者的重视。以南码头路街道为案例进行调查和研究,结合现有的交通政策、法规、规范及自行车停车场设计原则、选址要求和指标等,确定区域停放区面积和共享单车数量需求。将城市土地利用性质和道路规划布局现状与共享单车停放需求相结合,合理布局、规划共享单车停放区,对规范共享单车的停放和使用具有重要的现实意义。     

An equilibrium analysis of commuter parking in the era of autonomous vehicles

This study is the first in the literature to model the joint equilibrium of departure time and parking location choices when commuters travel with autonomous vehicles (AVs). With AVs, walking from parking spaces to the work location is not needed. Instead, AVs will drop off the commuters at the workplace and then drive themselves to the parking spaces. In this context, the equilibrium departure/arrival profile is different from the literature with non-autonomous vehicles (non-AVs). Besides modeling the commuting equilibrium, this study further develops the first-best time-dependent congestion tolling scheme to achieve the system optimum. Also, a location-dependent parking pricing scheme is developed to replace the tolling scheme. Furthermore, this study discusses the optimal parking supply to minimize the total system cost (including both the travel cost and the social cost of parking supply) under either user equilibrium or system optimum traffic flow pattern. It is found that the optimal planning of parking can be different from the non-AV situation, since the vehicles can drive themselves to parking spaces that are further away from the city center and walking of commuters is avoided. This paper sheds light on future parking supply planning and traffic management.     

旅游交通融合发展研究——以福建省为例

为深入落实"交通强国"战略和实现"全域旅游"目标,推动旅游与交通深度融合发展,需做好旅游交通体系发展的顶层设计。通过分析旅游交通面临的问题,提出了旅游交通融合发展的理念。将福建省作为案例,针对福建旅游交通网络面临的问题,提出了"快旅慢游"旅游交通体系的建设目标、实施方案及保障措施。依托高铁、民航、邮轮和公路等综合立体交通网络,提出了"一带三环多放射"旅游交通"快旅"通道的发展思路;通过特色旅游产品开发、旅游服务质量提升等方面,规划了"慢游"微循环旅游交通的发展方案,构建了福建省多层次、综合立体的旅游交通体系发展新格局。以期通过福建省旅游交通融合发展案例研究,为其他省市旅游交通融合发展借鉴参考。     

城市绿色出行内涵和评价指标体系框架研究

绿色出行发展的根本目的是为了实现城市交通可持续发展,实现出行"安全、畅通、高效、舒适、环保、节能",从而实现社会、经济、交通和环境的协调发展。本文通过对绿色出行的概念、内涵、特征和实现途径等相关理论进行解读,确定绿色出行系统的主要构成;采用计划行为理论、交通需求管理理论等多视角,对影响和制约城市绿色出行发展的关键因素进行分析和识别,并研究提出围绕保障能力、基础设施、运输装备、运营服务等方面的绿色出行评价指标体系框架。     

Singapore motorisation restraint and its implications on travel behaviour and urban sustainability

The example of Singapore shows that rapid urban and economic growth does not have to bring traffic congestion and pollution. Singapore has chosen to restrain car traffic demand due to its limited land supply. Transport policy based on balanced development of road and transit infrastructure and restraint of traffic has been consistently implemented for the past 30 years. Combined with land use planning, it resulted in a modern transport system, which is free from major congestion and provides users with different travel alternatives. As the economic growth caused a substantial increase in demand for cars, several pricing policies were introduced with the aim of restraining car ownership and usage. Growth of the vehicle population is now controlled and potentially congested roads are subject to road pricing. These measures help to keep the roads free from major congestion, maintain car share of work trips below 25% and keep the transport energy usage low. Although Singapore conditions are in many aspects unique, its travel demand experience can provide useful lessons for other rapidly growing cities in Asia.

Urban form and long-term fuel supply decline: A method to investigate the peak oil risks to essential activities

The issue of a peak in world oil supply has become a mainstream concern over the past several years. The petroleum geology models of post-peak oil production indicate supply declines from 1.5% to 6% per year. Travel requires fuel energy, but current transportation planning models do not include the impacts of constrained fuel supply on private travel demand. This research presents a method to assess the risk to activities due to a constrained fuel supply relative to projected unconstrained travel demand. The method assesses the probability of different levels of fuel supply over a given planning horizon, then calculates impact due to the energy supply not meeting the planning expectations. A new travel demand metric which characterizes trips as essential, necessary, and optional to wellbeing is used in the calculation. A case study explores four different urban forms developed from different future growth options for the urban development strategy of Christchurch, New Zealand to 2041. Probable fuel supply availability was calculated, and the risk to transport activities in the 2041 transport model was assessed. The results showed all the urban forms had significantly reduced trip numbers and lower energy mode distributions from the current planning projections, but the risk to activities differed among the planning options. Density is clearly one of the mitigating factors, but density alone does not provide a solution to reduced energy demand. The method clearly shows how risk to participation in activities is lower for an urban form which has a high degree of human powered and public transport access to multiple options between residential and commercial/industrial/service destinations. This analysis has led to new thinking about adaptation and reorganization of urban forms as a strategy for energy demand reduction rather than just densification.     

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.     

An impact analysis of traffic image information system on driver travel choice

A driver is one of the main components in a transportation system that influences the effectiveness of any active demand management (ADM) strategies. As such, the understanding on driver behavior and their travel choice is crucial to ensure the successful implementation of ADM strategies in alleviating traffic congestion, especially in city centres. This study aims to investigate the impact of traffic information dissemination via traffic images on driver travel choice and decision. A relationship of driver travel choice with respect to their perceived congestion level is developed by an integrated framework of genetic algorithm–fuzzy logic, being a new attempt in driver behavior modeling. Results show that drivers consider changing their travel choice when the perceived congestion level is medium, in which changing departure time and diverting to alternative roads are two popular choices. If traffic congestion escalates further, drivers are likely to cancel their trip. Shifting to public transport system is the least likely choice for drivers in an auto-dependent city. These findings are important and useful to engineers as they are required to fully understand driver (user) sensitivity to traffic conditions so that relevant active travel demand management strategies could be implemented successfully. In addition, engineers could use the relationships established in this study to predict drivers’ response under various traffic conditions when carrying out modeling and impact studies.     

Road pricing for congestion control with unknown demand and cost functions

It is widely recognized that precise estimation of road tolls for various pricing schemes requires a few pieces of information such as origin–destination demand functions, link travel time functions and users’ valuations of travel time savings, which are, however, not all readily available in practice. To circumvent this difficulty, we develop a convergent trial-and-error implementation method for a particular pricing scheme for effective congestion control when both the link travel time functions and demand functions are unknown. The congestion control problem of interest is also known as the traffic restraint and road pricing problem, which aims at finding a set of effective link toll patterns to reduce link flows to below a desirable target level. For the generalized traffic equilibrium problem formulated as variational inequalities, we propose an iterative two-stage approach with a self-adaptive step size to update the link toll pattern based on the observed link flows and given flow restraint levels. Link travel time and demand functions and users’ value of time are not needed. The convergence of the iterative toll adjustment algorithm is established theoretically and demonstrated on a set of numerical examples.     

A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction

This paper presents a Bayesian inference-based dynamic linear model (DLM) to predict online short-term travel time on a freeway stretch. The proposed method considers the predicted freeway travel time as the sum of the median of historical travel times, time-varying random variations in travel time, and a model evolution error, where the median is employed to recognize the primary travel time pattern while the variation captures unexpected supply (i.e. capacity) reduction and demand fluctuations. Bayesian forecasting is a learning process that revises sequentially the state of a priori knowledge of travel time based on newly available information. The prediction result is a posterior travel time distribution that can be employed to generate a single-value (typically but not necessarily the mean) travel time as well as a confidence interval representing the uncertainty of travel time prediction. To better track travel time fluctuations during non-recurrent congestion due to unforeseen events (e.g., incidents, accidents, or bad weather), the DLM is integrated into an adaptive control framework that can automatically learn and adjust the system evolution noise level. The experiment results based on the real loop detector data of an I-66 segment in Northern Virginia suggest that the proposed method is able to provide accurate and reliable travel time prediction under both recurrent and non-recurrent traffic conditions.