基于路网容量的停车需求预测方法

分析了停车需求交通出行O-D法与路网容量对停车需求的影响,以区域路网总容量及路网服务水平为约束,对停车需求的交通出行O-D预测方法进行修正,提出了一种基于路网容量的停车需求预测方法.应用结果表明该方法能够有效反映路网容量对停车需求预测的影响,是一种有效的停车需求预测方法.     

T-GIS技术在交通发生预测中的应用

交通运输系统规划的主要内容是对区域和城市运输系统进行预测和优化研究,它是交通GIS应用的重点领域.结合交通地理信息系统的基本概念,利用T-GIS进行发生与吸引交通量的预测是交通运输规划与GIS相结合的主要应用分支之一.以西安市为例,在TRANSCAD环境下进行交通发生量与吸引量的建模和预测,得到了直观、可靠的结果,可见T-GIS技术在交通领域具有广阔的应用前景.     

基于手机信令数据的常规公交站间OD识别

为识别手机用户乘坐常规公交的OD,结合公交车辆GPS轨迹,在考虑常规公交换乘行为的基础上,建立基于手机用户与车辆轨迹相似度的常规公交出行识别模型,以及站间OD概率模型.通过地铁出行识别,融合手机信令数据与IC卡数据,提取包含百万样本的公交与非公交出行数据,以此作为验证集.进一步分析各参数取值、出行距离、公交线路重复系数...     

基于多源数据的公交出行特征分析

为准确分析公交消费数据不完整情况下的公交出行特征,基于乘客上车刷卡数据、支付宝扫码数据及公交GPS数据,运用时空匹配法和出行链理论挖掘分析乘客上下车站点、公交线路OD矩阵、出行空间分布特性及消费时间分布特征。实际验证结果表明:1)使用IC卡和支付宝的乘客数量近似相等,使用现金人数较少,约占整体的6%;2)乘客出行次数在2次以下占总数的84%,换乘需求较少,公交可达性较高;3)高峰期消费次数均超过25000次/h,约占全天总数的23%,居民出行目的较为单一,大部分往返于居民区与办公商业区,与实际情况相符。     

引入旅游偏好的城际客运出行分布预测模型

针对目前城际客运出行分布预测过程中对城际旅游关联性考虑不足的问题，借助手机信 令数据，对长三角26个城市5A和4A级景点的城际游客客源地分布进行分析发现，在同一个城市 中，无论是从单个头部景点层面还是全部5A和4A级景点总体层面上来看，城际游客客源地分布 规律均较为相近，且在非节日的不同日期，这种分布也非常稳定。基于这种特性，借助亲景度指 标提出一个可以表征两个城市间旅游关联性的旅游偏好变量，并将其引入基本重力模型中构建 城际客运出行分布预测模型。基于长三角26个城市出行分布现状数据对模型系数进行标定后的 结果表明，模型拟合程度显著提高，标准误差大幅下降，75%的OD对预测结果得到改善。因此， 可以认为引入旅游偏好变量有利于提高城际客运出行分布预测模型的精度。     

An adaptive hawkes process formulation for estimating time-of-day zonal trip arrivals with location-based social networking check-in data

Location-Based Social Networking (LBSN) services, such as Foursquare, Facebook check-ins, and Geo-tagged Twitter tweets, have emerged as new secondary data sources for studying individual travel mobility patterns at a fine-grained level. However, the differences between human social behavioral and travel patterns can cause significant sampling bias for travel demand estimation. This paper presents a dynamic model to estimate time-of-day zonal trip arrival patterns. In the proposed model, the state propagation is formulated by the Hawkes process; the observation model implements LBSN sampling. The proposed model is applied to Foursquare check-in data collected from Austin, Texas in 2010 and calibrated with Origin-Destination (OD) data and time of day factor from Capital Area Metropolitan Planning Organization (CAMPO). The proposed model is compared with a simple aggregation model of trip purposes and time of day based on a prior daily OD estimation model using the LBSN data. The results illustrate the promising benefits of applying stochastic point process models and state-space modeling in time-of-day zonal arrival pattern estimation with the LBSN data. The proposed model can significantly reduce the number of parameters to calibrate in order to reduce the sampling bias of LBSN data for trip arrival estimation.     

Examining the effects of the built environment and residential self-selection on commuting trips and the related CO2 emissions: An empirical study in Guangzhou,China

Numerous studies have established the link between the built environment and travel behavior. However, fewer studies have focused on environmental costs of travel (such as CO₂ emissions) with respect to residential self-selection. Combined with the application of TIQS (Travel Intelligent Query System), this study develops a structural equations model (SEM) to examine the effects of the built environment and residential self-selection on commuting trips and their related CO₂ emissions using data from 2015 in Guangzhou, China. The results demonstrate that the effect of residential self-selection also exists in Chinese cities, influencing residents’ choice of living environments and ultimately affecting their commute trip CO₂ emissions. After controlling for the effect of residential self-selection, built environment variables still have significant effects on CO₂ emissions from commuting although some are indirect effects that work through mediating variables (car ownership and commuting trip distance). Specifically, CO₂ emissions are negatively affected by land-use mix, residential density, metro station density and road network density. Conversely, bus stop density, distance to city centers and parking availability near the workplace have positive effects on CO₂ emissions. To promote low carbon travel, intervention on the built environment would be effective and necessary.     

Individual trip rate transferability analysis based on a decision tree approach

Transferring trip rates to areas without local survey data is a common practice which is typically performed in an ad hoc fashion using household-based cross-classification tables. This paper applies a rule-based decision tree method to develop individual-level trip generation models for eight different trip purposes as defined in the US National Household Travel Survey in addition to daily vehicle miles traveled. For each trip purpose, the models are obtained by finding the best fitted statistical distribution to each of the final decision tree clusters while considering the correlation between the trip rates for other trip purposes. The rule-based models are sensitive to changes in demographics. The performance of the models is then tested and validated in a transferability application to the Phoenix Metropolitan Region. These models can be employed in a disaggregate microsimulation framework to generate trips with different purposes at the individual or household level.     

Estimation of population origin–interchange–destination flows on multimodal transit networks

Previous research has combined automated fare-collection (AFC) and automated vehicle-location (AVL) data to infer the times and locations of passenger origins, interchanges (transfers), and destinations on multimodal transit networks. The resultant origin–interchange–destination flows (and the origin–destination (OD) matrices that comprise those flows), however, represent only a sample of total ridership, as they contain only those journeys made using the AFC payment method that have been successfully recorded or inferred. This paper presents a method for scaling passenger-journey flows (i.e., linked-trip flows) using additional information from passenger counts at each station gate and bus farebox, thereby estimating the flows of non-AFC passengers and of AFC passengers whose journeys were not successfully inferred.The proposed method is applied to a hypothetical test network and to AFC and AVL data from London’s multimodal public transit network. Because London requires AFC transactions upon both entry and exit for rail trips, a rail-only OD matrix is extracted from the estimated multimodal linked-trip flows, and is compared to a rail OD matrix generated using the iterative proportional fitting method.