Connected variable speed limits control and car-following control with vehicle-infrastructure communication to resolve stop-and-go waves

The vision of intelligent vehicles traveling in road networks has prompted numerous concepts to control future traffic flow, one of which is the in-vehicle actuation of traffic control commands. The key of this concept is using intelligent vehicles as actuators for traffic control systems. Under this concept, we design and test a control system that connects a traffic controller with in-vehicle controllers via vehicle-to-infrastructure communication. The link-level traffic controller regulates traffic speeds through variable speed limits (VSL) gantries to resolve stop-and-go waves, while intelligent vehicles control accelerations through vehicle propulsion and brake systems to optimize their local situations. It is assumed that each intelligent vehicle receives VSL commands from the traffic controller and uses them as variable parameters for the local vehicle controller. Feasibility and effectiveness of the connected control paradigm are tested with simulation on a two-lane freeway stretch with intelligent vehicles randomly distributed among human-driven vehicles. Simulation shows that the connected VSL and vehicle control system improves traffic efficiency and sustainability; that is, total time spent in the network and average fuel consumption rate are reduced compared to (uncontrolled and controlled) scenarios with 100% human drivers and to uncontrolled scenarios with the same intelligent vehicle penetration rates.     

A State-of-the-Art Review: Developments in Utility Theory,Prospect Theory and Regret Theory to Investigate Travellers' Behaviour in Situations Involving Travel Time Uncertainty

Abstract

Despite the wide use of utility theory to model travellers' behaviour, the interest in non-expected utility theories has increased due to their potential to capture more realistic behaviour. A main question raised is whether travellers are better described as utility maximizers or should be qualified differently.

This paper presents a literature review on the use of expected utility theory (EUT), prospect theory (PT) and regret theory (RT) to model travellers' behaviour. Gaps in the literature are identified and a discussion about advantages and disadvantages of each theory is presented. A case study illustrates the differences between the theories.

Under certain conditions, PT and RT restrict themselves to EUT. Their added value, however, is the possibility of capturing loss aversion, risk aversion and risk-seeking (PT) and regret aversion (RT). On the practical level, the use of EUT is well established, while contributions of PT and RT are marginal. On the theoretical level, however, RT seems to be (marginally) more suitable to model travellers' behaviour, while EUT and PT are equally suitable. This suggests that the large use of EUT is highly influenced by its very tractable framework. We do not claim the superiority of any theory, but propose to compare them through a systematic review.     

Understanding bikeability: a methodology to assess urban networks

Transportation - A fully separated bicycle network from vehicular traffic is not realistic even for the most bicycle-friendly cities. Thus, all around the world urban cycling entails switching...     

Walking infrastructure design assessment by continuous space dynamic assignment modeling

For the planning and design of walking infrastructure, characterized by the fact that the pedestrians can choose their paths freely in two‐dimensional space, applicability of traditional discrete network models is limited. This contribution puts forward an approach for user‐optimal dynamic assignment in continuous time and space for analyzing for instance walking infrastructure in a two‐dimensional space. Contrary to network‐based approaches, the theory allows the traffic units to choose from an infinite non‐countable set of paths through the considered space. The approach first determines the continuous paths using a path choice model. Then, origin‐destination flows are assigned and traffic conditions are calculated. The approach to determine a user‐optimal assignment is heuristic and consists of a sequence of all‐or‐nothing assignments. An application example is presented, showing dynamic user equilibrium traffic flows through a realistic transfer station. The example is aimed at illustrating the dynamic aspects of the modeling approach, such as anticipation on expected flow conditions, and predicted behavior upon catching or missing a connection.     

Estimation of train dwell time at short stops based on track occupation event data: A study at a Dutch railway station

Train dwell time is one of the most unpredictable components of railway operations, mainly because of the varying volumes of alighting and boarding passengers. However, for reliable estimations of train running times and route conflicts on main lines, it is necessary to obtain accurate estimations of dwell times at the intermediate stops on the main line, the so‐called short stops. This is a great challenge for a more reliable, efficient and robust train operation. Previous research has shown that the dwell time is highly dependent on the number of boarding and alighting passengers. However, these numbers are usually not available in real time. This paper discusses the possibility of a dwell time estimation model at short stops without passenger demand information by means of a statistical analysis of track occupation data from the Netherlands. The analysis showed that the dwell times are best estimated for peak and off‐peak hours separately. The peak‐hour dwell times are estimated using a linear regression model of train length, dwell times at previous stops and dwell times of the preceding trains. The off‐peak‐hour dwell times are estimated using a non‐parametric regression model, in particular, the k‐nearest neighbor model. There are two major advantages of the proposed estimation models. First, the models do not need passenger flow data, which is usually impossible to obtain in real time in practice. Second, detailed parameters of rolling stock configuration and platform layout are not required, which makes the model more generic and eases implementation. A case study at Dutch railway stations shows that the estimation accuracy is 85.8%–88.5% during peak hours and 80.1% during off‐peak hours, which is relatively high. We conclude that the estimation of dwell times at short stop stations without passenger data is possible. Copyright © 2016 John Wiley & Sons, Ltd.     

Counting people in the crowd using social media images for crowd management in city events

Transportation - City events are getting popular and are attracting a large number of people. This increase needs for methods and tools to provide stakeholders with crowd size information for crowd...     

阿姆斯特丹城市道路线圈检测器布设方法研究

基于宏观基本图(Macroscopic Fundamental Diagram,MFD)的网络交通控制方法受到越来越多的国家的重视,它可以更有效的节约资源和提高效率。然而建立MFD需要以收集网络范围内详细的交通数据为前提,阿姆斯特丹的城市道路错综复杂,若在每个城市道路路段都布设线圈检测器需要巨大的经济支持和维护。基于Vissim的仿真实验的方法,建立阿姆斯特丹的MFD;通过实验方法确定城市道路中对于建立网络MFD起着显著的影响关键路段,为线圈检测器的布设提供了理论依据。     

Trip chain complexity: a comparison among latent classes of daily mobility patterns

This paper studies the relationship between trip chain complexity and daily travel behaviour of travellers. While trip chain complexity is conventionally investigated between travel modes, our scope is the more aggregated level of a person’s activity-travel pattern. Using data from the Netherlands Mobility Panel, a latent class cluster analysis was performed to group people with similar mode choice behaviour in distinct mobility pattern classes. All trip chains were assigned to both a travel mode and the mobility pattern class of the traveller. Subsequently, differences in trip chain complexity distributions were analysed between travel modes and between mobility pattern classes. Results indicate considerable differences between travel modes, particularly between multimodal and unimodal trip chains, but also between the unimodal travel modes car, bicycle, walking and public transport trip chains. No substantial differences in trip chain complexity were found between mobility pattern classes. Independently of the included travel modes, the distributions of trip chain complexity degrees were similar across mobility pattern classes. This means that personal circumstances such as the number of working hours or household members are not systematically translated into specific mobility patterns.