Modeling duration choice in space–time multi-state supernetworks for individual activity-travel scheduling

Multi-state supernetworks have been advanced recently for modeling individual activity-travel scheduling decisions. The main advantage is that multi-dimensional choice facets are modeled simultaneously within an integral framework, supporting systematic assessments of a large spectrum of policies and emerging modalities. However, duration choice of activities and home-stay has not been incorporated in this formalism yet. This study models duration choice in the state-of-the-art multi-state supernetworks. An activity link with flexible duration is transformed into a time-expanded bipartite network; a home location is transformed into multiple time-expanded locations. Along with these extensions, multi-state supernetworks can also be coherently expanded in space–time. The derived properties are that any path through a space–time supernetwork still represents a consistent activity-travel pattern, duration choice are explicitly associated with activity timing, duration and chain, and home-based tours are generated endogenously. A forward recursive formulation is proposed to find the optimal patterns with the optimal worst-case run-time complexity. Consequently, the trade-off between travel and time allocation to activities and home-stay can be systematically captured.     

Autonomous cars: The tension between occupant experience and intersection capacity

Systems that enable high levels of vehicle-automation are now beginning to enter the commercial marketplace. Road vehicles capable of operating independently of real-time human control under an increasing set of circumstances will likely become more widely available in the near future. Such vehicles are expected to bring a variety of benefits. Two such anticipated advantages (relative to human-driver vehicle control) are said to be increased road network capacity and the freeing up of the driver-occupant’s time to engage in their choice of leisurely or economically-productive (non-driving) tasks.In this study we investigate the implications for intersection capacity and level-of-service of providing occupants of automated (without real-time human control), autonomously-operating (without vehicle-to-X communication) cars with ride quality that is equivalent (in terms of maximum rates of longitudinal and lateral acceleration) to two types of rail systems: [urban] light rail transit and [inter-urban] high-speed rail. The literature suggests that car passengers start experiencing discomfort at lower rates of acceleration than car drivers; it is therefore plausible that occupants of an autonomously-operating vehicle may wish to instruct their vehicle to maneuver in a way that provides them greater ride comfort than if the vehicle-control algorithm simply mimicked human-driving-operation.On the basis of traffic microsimulation analysis, we found that restricting the dynamics of autonomous cars to the acceleration/deceleration characteristics of both rail systems leads to reductions in a signalized intersection’s vehicle-processing capacity and increases in delay. The impacts were found to be larger when constraining the autonomous cars’ dynamics to the more-restrictive acceleration/deceleration profile of high-speed rail. The scenarios we analyzed must be viewed as boundary conditions, because autonomous cars’ dynamics were by definition never allowed to exceed the acceleration/deceleration constraints of the rail systems. Appropriate evidence regarding motorists’ preferences does not exist at present; establishing these preferences is an important item for the future research agenda.This paper concludes with a brief discussion of research needs to advance this line of inquiry.     

Hierarchal assessment of noise pollution in urban areas – A case study

Noise pollution in urban areas has many harmful effects on the citizens. There are varieties of noise generation sources of which the traffic noise could be a major source. The point which is perhaps less noticed is that sound level is not the only parameter to indicate the extent and intensity of noise pollution. Situation of urban land uses, distribution of population centers and types of passages can deeply affect the concern on this environmental issue but not with a similar ratio. This article presents an overlaying technique to define noise prone areas using all different factors involved. A case study was carried out in the District 14 of Tehran Metropolitan City where there are busy streets and highways. For this purpose, the share of each criterion in noise pollution intensity was determined using Analytical Hierarchy Process (AHP). Afterwards, the map layers were overlaid based upon the relative importance of the criteria to get the final map on which the noise prone areas are specified. The developed method could be used as a tool for indirect estimation of noise pollution by which instead of direct measurement of the equivalent sound level, it would be possible to predict noise susceptible areas considering the most important influential factors.     

Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach

Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.     

桥梁模板施工技术应用性研究

桥梁模板是桥梁施工中的重要工具。通过搜集国内外桥梁模板资料,研究总结并提出解决模板工程在桥梁施工中存在各种问题的办法,将对提高工程质量、加快施工速度、提高劳动生产率、降低工程成本和实现桥梁工程的文明施工起到一定的推动作用。     

Mental models of driving and speed: biases,choices and reality

This paper provides a review of research performed by Svenson with colleagues and others work on mental models and their practical implications. Mental models describe how people perceive and think about the world including covariances and relationships between different variables, such as driving speed and time. Research on mental models has detected the time-saving bias [Svenson, O. (1970). A functional measurement approach to intuitive estimation as exemplified by estimated time savings. Journal of Experimental Psychology, 86, 204–210]. It means that drivers relatively overestimate the time that can be saved by increasing speed from an already high speed, for example, 90–130?km/h, and underestimate the time that can be saved by increasing speed from a low speed, for example, 30–45?km/h. In congruence with this finding, mean speed judgments and perceptions of mean speeds are also biased and higher speeds given too much weight and low speeds too little weight in comparison with objective reality. Replacing or adding a new speedometer in the car showing min per km eliminated or weakened the time-saving bias. Information about braking distances at different speeds did not improve overoptimistic judgments of braking capacity, but information about collision speed with an object suddenly appearing on the road did improve judgments of braking capacity. This is relevant to drivers, politicians and traffic regulators.     

A copula-based approach for estimating the travel time reliability of urban arterial

Estimating the travel time reliability (TTR) of urban arterial is critical for real-time and reliable route guidance and provides theoretical bases and technical support for sophisticated traffic management and control. The state-of-art procedures for arterial TTR estimation usually assume that path travel time follows a certain distribution, with less consideration about segment correlations. However, the conventional approach is usually unrealistic because an important feature of urban arterial is the dependent structure of travel times on continuous segments. In this study, a copula-based approach that incorporates the stochastic characteristics of segments travel time is proposed to model arterial travel time distribution (TTD), which serves as a basis for TTR quantification. First, segments correlation is empirically analyzed and different types of copula models are examined. Then, fitting marginal distributions for segment TTD is conducted by parametric and non-parametric regression analysis, respectively. Based on the estimated parameters of the models, the best-fitting copula is determined in terms of the goodness-of-fit tests. Last, the model is examined at two study sites with AVI data and NGSIM trajectory data, respectively. The results of path TTD estimation demonstrate the advantage of the proposed copula-based approach, compared with the convolution model without capturing segments correlation and the empirical distribution fitting methods. Furthermore, when considering the segments correlation effect, it was found that the estimated path TTR is more accurate than that by the convolution model.     

Travel analytics: Understanding how destination choice and business clusters are connected based on social media data

Understanding how destination choice and business clusters are connected is of great importance for designing sustainable cities, fostering flourishing business clusters, and building livable communities. As sharing locations and activities on social media platforms becomes increasingly popular, such data can reveal destination choice and activity space which can shed light on human-environment relationships. To this end, this research models the relationship between characteristics of business clusters and check-in activities from Los Angeles County, California. Business clusters are analyzed via two lenses: the supply side (employment data by industry) and the demand side (on-line check-in data). Spatial and statistical analyses are performed to understand how land use and transportation network features affect the popularity of the identified clusters and their relationships. Our results suggest that a cluster with more employment opportunities and more types of employment is associated with more check-ins. A business cluster that has access to parks or recreational services is also more popular. A business cluster with a longer road network and better connectivity of roads is associated with more check-ins. The visualization of the common visitors between clusters reveals that there are a few clusters with outstanding strong ties, while most have modest ties with each other. Our findings have implications on the influence of urban design on the popularity of business clusters.     

基于客流需求的城市轨道交通动态时刻表优化模型

为使城市轨道交通列车运行时刻表更贴合客流需求,依据不断变化的客流需求确定每列车的发车时刻和停站时间,采用多目标优化方法构建以乘客出行时间费用和列车运行时间费用最小为目标、列车发车时刻和停站时间为决策变量的城市轨道交通动态时刻表优化模型,并采用粒子群算法求解。以广州地铁13号线为例进行验证,结果表明优化后的时刻表更满足客流需求,能有效地提高乘客出行效率,具有更好的动态适应性。     

基于信令数据的出行强度影响因素分析模型

为探究城市交通出行强度影响因素及不同因素的影响程度，本文从土地利用与交通基础设施建设两方面出发，分析包括土地利用混合指数，职住混合率熵指数，公共交通站点 500 m覆盖率，路网可达性等17个指标与出行强度的相关关系；基于相关系数和拟合优度分析，提取7个与出行强度强相关指标，基于所识别指标构建北京市中心城区出行强度多元回归模型.模型结果表明，职住混合率熵指数对出行强度的影响最为显著，公共交通站点覆盖率对出行强度的影响比道路网密度和可达性更为明显.此外，给出单一土地利用/交通基础设施指标对出行强度拟合结果的离群特征分析方法，用于评估不同区域基础设施供给与交通出行需求之间的平衡关系.