The cost of equity: Assessing transit accessibility and social disparity using total travel cost

Social equity is increasingly incorporated as a long-term objective into urban transportation plans. Researchers use accessibility measures to assess equity issues, such as determining the amount of jobs reachable by marginalized groups within a defined travel time threshold and compare these measures across socioeconomic categories. However, allocating public transit resources in an equitable manner is not only related to travel time, but also related to the out-of-pocket cost of transit, which can represent a major barrier to accessibility for many disadvantaged groups. Therefore, this research proposes a set of new accessibility measures that incorporates both travel time and transit fares. It then applies those measures to determine whether people residing in socially disadvantaged neighborhoods in Montreal, Canada experience the same levels of transit accessibility as those living in other neighborhoods. Results are presented in terms of regional accessibility and trends by social indicator decile. Travel time accessibility measures estimate a higher number of jobs that can be reached compared to combined travel time and cost measures. However, the degree and impact of these measures varies across the social deciles. Compared to other groups in the region, residents of socially disadvantaged areas have more equitable accessibility to jobs using transit; this is reflected in smaller decreases in accessibility when fare costs are included. Generating new measures of accessibility combining travel time and transit fares provides more accurate measures that can be easily communicated by transportation planners and engineers to policy makers and the public since it translates accessibility measures to a dollar value.     

Multi-step prediction of experienced travel times using agent-based modeling

This paper develops an agent-based modeling approach to predict multi-step ahead experienced travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in a decision-making system. Each expert predicts the travel time for each time interval according to experiences from a historical dataset. A set of agent interactions is developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents associated with negligible weights with new agents. Consequently, the aggregation of each agent’s recommendation (predicted travel time with associated weight) provides a macroscopic level of output, namely the predicted travel time distribution. Probe vehicle data from a 95-mile freeway stretch along I-64 and I-264 are used to test different predictors. The results show that the agent-based modeling approach produces the least prediction error compared to other state-of-the-practice and state-of-the-art methods (instantaneous travel time, historical average and k-nearest neighbor), and maintains less than a 9% prediction error for trip departures up to 60 min into the future for a two-hour trip. Moreover, the confidence boundaries of the predicted travel times demonstrate that the proposed approach also provides high accuracy in predicting travel time confidence intervals. Finally, the proposed approach does not require offline training thus making it easily transferable to other locations and the fast algorithm computation allows the proposed approach to be implemented in real-time applications in Traffic Management Centers.     

Value of travel time reliability: A review of current evidence

Travel time reliability is a fundamental factor in travel behavior. It represents the temporal uncertainty experienced by travelers in their movement between any two nodes in a network. The importance of the time reliability depends on the penalties incurred by the travelers. In road networks, travelers consider the existence of a trip travel time uncertainty in different choice situations (departure time, route, mode, and others). In this paper, a systematic review of the current state of research in travel time reliability, and more explicitly in the value of travel time reliability is presented. Moreover, a meta-analysis is performed in order to determine the reasons behind the discrepancy among the reliability estimates.     

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.     

Aggregating mobile object trajectories: cumulative time geographic density estimation for GPS data

We present new approaches that expand upon the time geographic density estimation (TGDE) framework previously employed to estimate potential path trees. In the past, TGDE metrics have identified possible locations an individual moving object may have passed between, given known origin and destination points. This paper utilizes a new form of TGDE to investigate taxicab GPS traces over a specified time horizon with position ‘gaps’. To this end, we propose a new extension to the TGDE framework, TGDE-C, which is used to determine the cumulative TGDE values for a group of GPS traces, at a given location. These metrics are applied to multiple taxis and allow for time of day analysis. Additionally, we combine these new extensions with existing TGDE metrics that allow us to determine how accessible individual or groups of vehicles are to urban opportunities.     

盾构隧道近接浅埋式矩形隧道合理净距的研究

以正在建设中的深圳地铁11号线南山至前海湾站小净距重叠区间隧道为研究对象,按先明挖修建桂庙路矩形闭合框架隧道,后进行地铁盾构推进的顺序,采用FLAC有限差分程序对小净距重叠隧道进行弹塑性分析,对多种工况下的围岩位移、夹岩应力、塑性区分布等进行分析,提出矩形闭合框架隧道的管幕作用,其作用影响范围大致沿Mohr-Coulomb模型剪切破坏面(45°+φ/2)的角度,以矩形隧道宽为底边的闭合等腰三角形区域。在此区域内的地下结构均受管幕作用的影响,且距离矩形闭合框架隧道越近管幕作用越明显,在综合考虑了矩形隧道管幕作用和隧道之间近接效应的情况下,确定隧道之间的合理净距。     

基于特征的全船焊接工时聚类分析

以某型船全船焊接信息为样本,首先验证了数据样本服从对数正态分布;再以距离作为度量标准,对焊接信息进行基于不同距离测量方法的聚类分析,并重点对比平方欧氏距离和夹角余弦距离的聚类树形图,得出全船焊接信息合理的聚类数目;最后对每个类进行物理信息的解释,得到完整实用的基于特征的全船焊接工时估算标准,为新型船经济性论证及方案选型提供快捷、可靠的依据。     

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针对国内多模式公交线网规划缺乏依据的状况,以大城市多模式公交线网为研究对象,首先分析了城市居民的公交出行距离特征,借助双参数韦布尔分布建立了城市居民公交出行距离分布模型;基于广义出行费用,建立了各模式公交线网优势出行距离模型;基于供需平衡思想,构建了大城市多模式公交线网级配模型.以哈尔滨公交线网为例,测算得到了哈尔滨市2020年各模式公交线网的合理规模与级配关系,结果表明,测算的结果与2020年的预期需求较一致,说明建立的级配模型是合理可行的.     

牵引网微机保护整定的探讨

通过对牵引网负荷电流、短路电流中的谐波含量的分析和存在的谐波含量的不同点,利用两种电流中的综合谐波含量作为控制量,可以大大提高牵引网距离保护、高阻接地保护躲过牵引重负荷,再生负荷,启动电流及励磁涌流的能力,对于保护整定,通过探讨提出了整定原则和方法。     

铁路旅客旅行换乘方案优选算法

为给铁路旅客选择合理的旅行方案提供参考,以旅行目标值与换乘目标值之和最小作为目标函数值,考虑旅客从起点站出发、最终到达终到站、在中转站进出平衡的约束条件,建立旅客旅行换乘方案选择问题的数学模型。分别给出旅行时间、换乘次数、票价、距离、到发时刻和综合指数6种目标权值的确定方法,提出最短路法和列车匹配法2种求解方法。最短路法是通过构造并简化旅客运输网络,求出网络上若干条次短路,再根据各条次短路上列车的接续,构造列车换乘方案网络图,根据该网络的目标权值确定最短路,得到最优换乘方案。列车匹配法是根据列车运行图信息直接搜索发、到站的接续列车集合,求其两列车对应的重合停车站,根据重合停车站集合的情况选择迭代步数,剔除显然不利方案,构成换乘方案,根据方案的权值进行比选,得到最优换乘方案。对2种算法均用C#编程实现,并用现有的全路客票数据进行检算。计算结果表明:2种算法均能得到最优换乘方案;列车匹配法所用计算时间比最短路法少160 ms。