快乐“小狮子”

初入家门 2005年8月20日是个值得纪念的日子，我终于迎来了我可爱的小狮子。 买车的想法由来已久，早已习惯了舒适慵懒的学生生活，一朝面对拥挤不堪的上班路途——两次公交车一次地铁，耗时个半小时，我得寻求出路。     

Maximum acceptable gap between urban-guided transport vehicles and platforms for persons with disabilities: the findings of an experimental study conducted to inform policy decisions

The French ministry responsible for transport has commissioned a study aimed at providing data to determine the maximum gap size between vehicle and platform for guided transport vehicles, which would become a legal requirement. Specific experimental conditions were created, with a physical mock-up simulating a tramway and a platform and providing several gap configurations (between 20 and 50?mm for the horizontal gap and between 20 and 75?mm for the vertical step), and an experimental design based on the negotiation of gaps by wheelchairs users (mainly manual or electric powered). The experiment was conducted with 46 participants with different functional abilities. Data were collected on performance and gap negotiation time. The results of this study indicate that despite the failures observed for the 50?×?50?mm gap size during the experiment, most wheelchair users who took part in the test have successfully negotiated this gap.     

Machine learning for multi-jurisdictional optimal traffic corridor control

Urban traffic corridors are often controlled by more than one agency. Typically in North America, a state of provincial transportation department controls freeways while another agency at the municipal or city level controls the nearby arterials. While the different segments of the corridor fall under different jurisdictions, traffic and users know no boundaries and expect seamless service. Common lack of coordination amongst those authorities due to lack of means for information exchange and/or possible bureaucratic ‘institutional grid-lock’ could hinder the full potential of technically-possible integrated control. Such institutional gridlock and related lack of timely coordination amongst the different agencies involved can have a direct impact on traffic gridlock. One potential solution to this problem is through integrated automatic control under intelligent transportation systems (ITS). Advancements in ITS and communication technology have the potential to considerably reduce delay and congestion through an array of network-wide traffic control and management strategies that can seamlessly cross-jurisdictional boundaries. Perhaps two of the most promising such control tools for freeway corridors are traffic-responsive ramp metering and/or dynamic traffic diversion possibly using variable message signs (VMS). Technically, the use of these control methods separately might limit their potential usefulness. Therefore, integrated corridor control using ramp metering and VMS diversion simultaneously might be synergetic and beneficial. Motivated by the above problem and potential solution approach, the aim of the research presented in this paper is to develop a self-learning adaptive integrated freeway-arterial corridor control for both recurring and non-recurring congestion. The paper introduces the use of reinforcement learning, an Artificial Intelligence method for machine learning, to provide optimal control using ramp metering and VMS routing in an integrated agent for a freeway-arterial corridor. Reinforcement learning is an approach whereby the control agent directly learns optimal strategies via feedback reward signals from its environment. A simple but powerful reinforcement learning method known as Q-learning is used. Results from an elaborate simulation study on a key corridor in Toronto are very encouraging and discussed in the paper.