Highway Capacity Manual改訂の動向--テイラ-教授の講演より

The Statistics of Canada

Sustainability assessment is being increasingly viewed as an important tool to aid in the shift towards sustainability. However, this is a new and evolving concept and there remain very few examples of effective sustainability assessment processes implemented anywhere in the world. Sustainability assessment is often described as a process by which the implications of an initiative on sustainability are evaluated, where the initiative can be a proposed or existing policy, plan, programme, project, piece of legislation, or a current practice or activity. However, this generic definition covers a broad range of different processes, many of which have been described in the literature as 'sustainability assessment'. This article seeks to provide some clarification by reflecting on the different approaches described in the literature as being forms of sustainability assessment, and evaluating them in terms of their potential contributions to sustainability. Many of these are actually examples of 'integrated assessment', derived from environmental impact assessment (EIA) and strategic environmental assessment (SEA), but which have been extended to incorporate social and economic considerations as well as environmental ones, reflecting a 'triple bottom line' (TBL) approach to sustainability. These integrated assessment processes typically either seek to minimise 'unsustainability', or to achieve TBL objectives. Both aims may, or may not, result in sustainable practice. We present an alternative conception of sustainability assessment, with the more ambitious aim of seeking to determine whether or not an initiative is actually sustainable. We term such processes 'assessment for sustainability'. 'Assessment for sustainability' firstly requires that the concept of sustainability be well-defined. The article compares TBL approaches and principles-based approaches to developing such sustainability criteria, concluding that the latter are more appropriate, since they avoid many of the inherent limitations of the triple-bottom-line as a conception of sustainability.

Conceptualising sustainability assessment

https://www.tandfonline.com/doi/pdf/10.1080/15472450.2017.1291351

Policy and society related implications of automated driving: A review of literature and directions for future research

Population is steadily increasing worldwide, resulting in intractable traffic congestion in dense urban areas. Adaptive traffic signal control (ATSC) has shown strong potential to effectively alleviate urban traffic congestion by adjusting signal timing plans in real time in response to traffic fluctuations to achieve desirable objectives (e.g., minimize delay). Efficient and robust ATSC can be designed using a multiagent reinforcement learning (MARL) approach in which each controller (agent) is responsible for the control of traffic lights around a single traffic junction. Applying MARL approaches to the ATSC problem is associated with a few challenges as agents typically react to changes in the environment at the individual level, but the overall behavior of all agents may not be optimal. This paper presents the development and evaluation of a novel system of multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC). MARLIN-ATSC offers two possible modes: 1) independent mode, where each intersection controller works independently of other agents; and 2) integrated mode, where each controller coordinates signal control actions with neighboring intersections. MARLIN-ATSC is tested on a large-scale simulated network of 59 intersections in the lower downtown core of the City of Toronto, ON, Canada, for the morning rush hour. The results show unprecedented reduction in the average intersection delay ranging from 27% in mode 1 to 39% in mode 2 at the network level and travel-time savings of 15% in mode 1 and 26% in mode 2, along the busiest routes in Downtown Toronto.

Multiagent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLIN-ATSC): Methodology and Large-Scale Application on Downtown Toronto

This article presents the development of an adaptive signal control with Light Rail Transit (LRT) pre-emption at a single intersection. The algorithm that was developed selects the signal plan and the phase length at the end of each cycle according to the prevailing traffic and the presence of LRT preemption. The LRT preemption was extended to include an LRT travel-time prediction feature. An integrated transit and traffic corridor was modeled and calibrated based on extensive data collection efforts. The model was validated by comparing the actual observed and simulated data for both the LRT and vehicular traffic. An intensive sensitivity analysis was also conducted to examine the developed approach under varying traffic conditions and for different LRT operation parameters. The new adaptive signal control logic was shown to be effective in decreasing delays at the intersection compared to the existing pre-timed signal control operation. The adaptive signal control logic that was developed and tested in this article has outperformed its pre-timed signal control counterpart for all of the examined LRT dispatch rates and most of the increased congested conditions.

Adaptive Signal Control with LRT Preemption

Modelling of traffic flow lacking lane-adherence, with variable vehicle sizes and speeds, is a pressing issue in the simulation field. Commercially available software is not designed for modelling such traffic, which is characteristic of developing countries. A recently developed method is to model traffic as grains of sand moving through a porous medium. This approach is explored and expanded in this work to consider multiple vehicle classes and potential uses in microsimulation. A microsimulation model is developed using an object-oriented approach and compared with existing work in the field. The developed approach, found to fit with the porous flow approach, is shown to properly reflect the traffic flow impacts associated with introducing additional vehicle classes. Fundamental flow diagrams are presented for heterogeneous traffic, which fit a Daganzo approximation. It is determined that several changes are required to the theoretical framework to accurately represent traffic characteristics before viable commercial application.

Microsimulating Heterogeneous Traffic: An Implementation of the Porous Flow Approach

The problem of collisions on local roads has received little specific attention despite the considerable number of such collisions that occur each year. First part of this study identifies the fact...

Collisions on local roads: model development and policy level scenario analysis

Severe traffic congestion on urban arterials causes delays and extra travel time when the signal control systems are incapable of responding to high traffic fluctuations. This is especially the case during non-recurrent traffic congestion that is due to unexpected events such as incidents, construction, weather, etc. Pre-timed signal systems and actuated controllers cannot accommodate these types of traffic variation, due to a fixed signal plan. The objective of this research is the development of a responsive signal control strategy, which responds to varying traffic demand that arises from non-recurrent traffic congestion. Case-based reasoning (CBR), a technique in artificial intelligence (AI), was used to formulate a control strategy that the author called RESSICA (RESponsive SIgnal Control for Arterial), which matches the traffic pattern and assigns a corresponding timing plan from its library with minimal re-computation. RESSICA was tested in a corridor network with four signalized intersections under various levels of non-recurrent congestion scenarios. The results showed that RESSICA outperformed the existing pre-timed/actuated signal controller, in terms of reducing travel time, delay, stop delay and intersection delay in the study area.

Responsive Signal Control for Non-recurrent Traffic Congestion on an Arterials

Shanghai, China, has the largest metro system in the world, with a network length of more than 550 km. Both Shanghai and Beijing are among the top five cities in terms of ridership, and some of the most important components of their metro systems are the ring transit lines. Many other cities, in China and elsewhere, also envision a ring transit line for their future rail transit networks. A previously developed analytical model for the long-range planning of ring transit lines was used in the comparison of the current alignment of the Shanghai ring line with the optimized model output, and a second ring transit line was recommended for the future Shanghai network. The findings suggest that the alignment of an existing ring line would affect the optimal alignment of the second ring line. In addition, if an outer ring line exists (or is planned to be constructed), the optimal location of the inner ring line might not be its current location. Furthermore, a sensitivity analysis was conducted to test the impact of changes on demand, value of time, and passenger ride cost on the second ring line. Zones that would benefit most from introduction of the second ring line were also determined. Although the case study presents the Shanghai ring lines, the outcomes provide useful information for other cities that are considering the expansion of their transit network with a first or second ring line. Unlike simulations and agent-based models, the model presented in this study is easily transferable to any transit network.

Lina Kattan

Papers

Adaptive Signal Control with LRT Preemption

Microsimulating Heterogeneous Traffic: An Implementation of the Porous Flow Approach

Collisions on local roads: model development and policy level scenario analysis

Responsive Signal Control for Non-recurrent Traffic Congestion on an Arterials

Planning Urban Ring Rail Transit Lines: A Case Study of Shanghai, China