Platoons of connected vehicles can double throughput in urban roads

Intersections are the bottlenecks of the urban road system because an intersection’s capacity is only a fraction of the maximum flows that the roads connecting to the intersection can carry. This capacity can be increased if vehicles cross the intersections in platoons rather than one by one as they do today. Platoon formation is enabled by connected vehicle technology. This paper assesses the potential mobility benefits of platooning. It argues that saturation flow rates, and hence intersection capacity, can be doubled or tripled by platooning. The argument is supported by the analysis of three queuing models and by the simulation of a road network with 16 intersections and 73 links. The queuing analysis and the simulations reveal that a signalized network with fixed time control will support an increase in demand by a factor of (say) two or three if all saturation flows are increased by the same factor, with no change in the control. Furthermore, despite the increased demand vehicles will experience the same delay and travel time. The same scaling improvement is achieved when the fixed time control is replaced by the max pressure adaptive control. Part of the capacity increase can alternatively be used to reduce queue lengths and the associated queuing delay by decreasing the cycle time. Impediments to the control of connected vehicles to achieve platooning at intersections appear to be small.     

我国城市轨道交通网络运营盈利能力的实证分析

为从财务可持续性角度探讨城市轨道交通网络盈亏平衡问题，本文建立了以票款净收入和运营成本为基础的网络运营盈亏平衡模型，定量分析票价率、客运强度、票价优惠策略等要素对运营企业盈利能力的影响。实证研究表明：我国不同城市票价率水平差异较大，多数城市体验了初期线网扩张带来的网络化运营红利；但随着网络进一步扩大到市区边缘或郊区，多数城市网络总规模超过300 km后，全网平均客流强度呈下降态势，企业盈利能力有所下降，政府补贴压力增加。由于城市轨道交通在解决通勤出行中的公益性定位，以及不同城市居民人均可支配收入水平和城市政府财政收入的差异，应进一步研究不同城市经济发展水平下票价水平、线网规模及技术制式、不同线路功能定位的关系，以提高轨道交通网络运营盈利能力，促进我国城市轨道交通行业的可持续发展。     

Dynamic congestion pricing with day-to-day flow evolution and user heterogeneity

This paper investigates evolutionary implementation of congestion pricing schemes to minimize the system cost and time, measured in monetary and time units, respectively, with the travelers’ day-to-day route adjustment behavior and their heterogeneity. The travelers’ heterogeneity is captured by their value-of-times. First, the multi-class flow dynamical system is proposed to model the travelers’ route adjustment behavior in a tolled transportation network with multiple user classes. Then, the stability condition and properties of equilibrium is examined. We further investigate the trajectory control problem via dynamic congestion pricing scheme to derive the system cost, time optimum, and generally, Pareto optimum in the sense of simultaneous minimization of system cost and time. The trajectory control problem is modeled by a differential–algebraic system with the differential sub-system capturing the flow dynamics and the algebraic one capturing the pricing constraint. The explicit Runge–Kutta method is proposed to calculate the dynamic flow trajectories and anonymous link tolls. The method allows the link tolls to be updated with any predetermined periods and forces the system cost and/or time to approach the optimum levels. Both analytical and numerical examples are adopted to examine the efficiency of the method.     

基于PTC材料加热的柴油喷油器模拟

文章选用居里温度为60℃的陶瓷基PTC材料作为加热元件,利用流体力学计算软件Fluent模拟了两种不同燃油流量下用PTC材料加热柴油的温度场变化。仿真结果表明,利用PTC材料加热可有效提高燃油温度,改善冷启动性能,且随着燃油流量的增加,喷油器出口温度呈下降趋势。     

铁路客运站应对高峰客流的对策研究

通过分析铁路客运站应对客流高峰存在的问题,认为应从人流引导和客运站规划方面进行解决.在人流引导方面,客运站应该做好提前客流预测、加强员工培训、协调其他运输部门以及加大先进设施的运用；在客运站规划方面,通过分析国外先进客运站实例,总结相应经验,为铁路客运站应对高峰客流提供参考.     

铁路路堑挡雪墙设计参数优化数值模拟

风吹雪往往在铁路路堑地段形成较厚的积雪,掩埋线路,影响行车速度,危及行车安全,研究其具有重要的现实意义。基于FLUENT软件,模拟研究不同挡雪墙高度、不同风速下,挡雪墙背风侧风雪两相流的运动特性及挡雪墙参数优化设计。研究表明,风雪流初始速度一定时,挡雪墙背风侧积雪宽度随挡雪墙高度增大而变大,沉积在床面上的雪粒更多,阻雪效果越好;挡雪墙高度一定时,背风侧积雪宽度随风雪流速度的增加逐渐增大,挡雪墙距线路的距离也应越大。在综合考虑工程造价和挡雪效果的基础上,挡雪墙设计时,高度宜在2.5~3.5m,高度越高,风速越大,挡雪墙距线路的距离应越大,一般在20~35m即可。     

机场陆侧交通设施竖向布局模式研究

轨道交通设施引入机场后使机场陆测交通设施竖向布局设计更加复杂多样。分析了大型机场轨道交通线路走向与航站楼平面布局的关系,在剖析国内外各种实例的基础上,提出机场陆侧交通设施竖向布局模式;对各种模式的优缺点、换乘设施布局、旅客流程组织进行比较分析。     

Experienced travel time prediction for congested freeways

Travel time is an important performance measure for transportation systems, and dissemination of travel time information can help travelers make reliable travel decisions such as route choice or departure time. Since the traffic data collected in real time reflects the past or current conditions on the roadway, a predictive travel time methodology should be used to obtain the information to be disseminated. However, an important part of the literature either uses instantaneous travel time assumption, and sums the travel time of roadway segments at the starting time of the trip, or uses statistical forecasting algorithms to predict the future travel time. This study benefits from the available traffic flow fundamentals (e.g. shockwave analysis and bottleneck identification), and makes use of both historical and real time traffic information to provide travel time prediction. The methodological framework of this approach sequentially includes a bottleneck identification algorithm, clustering of traffic data in traffic regimes with similar characteristics, development of stochastic congestion maps for clustered data and an online congestion search algorithm, which combines historical data analysis and real-time data to predict experienced travel times at the starting time of the trip. The experimental results based on the loop detector data on Californian freeways indicate that the proposed method provides promising travel time predictions under varying traffic conditions.