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

Planned service disruptions (PSDs) of light rail transit (LRT) improve service reliability, extend infrastructure’s life, and reduce the frequency and impact of unplanned service disruption caused ...

https://journals.sagepub.com/doi/pdf/10.1177/03611981211012421

Transit Users’ Mode Choice Behavior During Light Rail Transit Short-Term Planned Service Disruption:

Applications of Passive GPS Data to Characterize the Movement of Freight Trucks - A Case Study in the Calgary Region of Canada

In this paper, a network-wide anticipatory control (AC) framework, incorporating drivers' route choice behaviour, is proposed The proposed AC, consisting of two main levels of control and route ch

A network-wide anticipatory control of an urban network using macroscopic fundamental diagram

The hourly traffic flows between various origin–destination (OD) pairs fluctuate by time of day and day of the year. These multi-period OD demands are statistically correlated with one another because of the inter-relationships of travel patterns over time. In this paper, with a focus on the covariance relationship of OD demands in multiple periods, a novel model is proposed for optimizing the allocations of multi-type traffic sensors by minimizing the uncertainty of OD demand estimates. In the proposed model, both the number and locations of multi-type traffic sensors, including point sensors and automatic vehicle identification (AVI) sensors, are optimized simultaneously with consideration of budget and associated constraints. The mathematical properties of the proposed model are studied to show the significance of multi-period OD flow covariance in the sensor location problem and to examine the trade-off between point sensors and AVI sensors. The firefly algorithm is adapted to solve the problem of multi-type traffic sensor locations for multi-period OD demand estimation. To enhance the estimation efficiency, a Kalman filter method based on the principal component analysis is adopted to extract the essential features of the OD demands and then estimate multi-period OD demand. Numerical examples are presented to demonstrate the effects of OD demand covariance in multiple periods for the multi-type sensor allocation problem. 

Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects

The path flow reconstruction problem is used to determine the minimum set of links that must be equipped with traffic monitoring devices to identify vehicle paths in a road network. This study addresses the path flow reconstruction problem in a connected vehicles (CVs) environment. Unlike traditional sensors that can observe both CVs and non-connected vehicles (NCVs), CV-enabled infrastructures, known as roadside units (RSUs), can only identify CVs on roads through vehicle to infrastructure (V2I) communications. They can, however, provide critical traffic information, including traces of the historical trajectories of CVs and possibly the desired path to a destination, thereby inferring partial information on links that are not directly covered by RSUs. RSUs have an “area” rather than a “point” coverage capability. This allows them to simultaneously monitor more than one link. We mathematically developed four variant formulations for the path flow reconstruction problem to optimally locate either a network’s RSU or a mix of the network’s RSUs and automatic vehicle identification (AVI) sensors. The first two models assume 100% market penetration of CVs and the first model determines the links that should be directly covered by RSUs in a road network. While the desired path to a destination is assumed to be unknown. This model determines an upper bound for the number of RSUs required to fully reconstruct path flows by using each RSU to directly cover a link. To consider the coverage and range of the RSU (where RSU can cover more than one link) and to minimize the total cost, Model II optimizes the locations of traditional AVI sensors and RSUs. This allows the model to capitalize on the RSUs’ area and indirect coverage features to fully reconstruct path flow in a road network. Model III considers the gradual deployment of CVs and thus the prevailing mixed traffic environment consisting of both CVs and NCVs. Accordingly, this model relaxes the assumption of a 100% penetration rate of CVs and maximizes the path flow information gain subject to a budget constraint in a mixed traffic environment. Finally, Model IV explores the infrastructure to infrastructure (I2I) communication capability among RSUs, further maximizing the traffic flow information gain of CVs while guaranteeing full path flow reconstruction. The results suggest that fewer RSUs than AVI sensors are required to reach full path flow reconstruction in a road network. The level of unique path flow information obtained from RSUs is also considerably higher than what can be obtained from AVI sensors. It is demonstrated that, in mixed traffic conditions, the coverage range of RSUs and their cost compared to AVI sensors can significantly affect the deployment of either type of sensing devices for maximized path flow information gain. 

Lina Kattan

Papers

Transit Users’ Mode Choice Behavior During Light Rail Transit Short-Term Planned Service Disruption:

Applications of Passive GPS Data to Characterize the Movement of Freight Trucks - A Case Study in the Calgary Region of Canada

A network-wide anticipatory control of an urban network using macroscopic fundamental diagram

Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects

Optimal roadside units location for path flow reconstruction in a connected vehicle environment