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

Historically, most demographic data for states and substate areas were collected from the long version of the decennial census questionnaire. A “snapshot” of the characteristics of the population on the April 1 census date was available once every 10 years. The long form of the decennial census has been replaced by the American Community Survey (ACS) that has been conducted on an ongoing basis for the entire country since 2005. Instead of a snapshot in which all of the data are gathered at one time, the ACS aggregates data collected over time, making the results more difficult to interpret. However, the ACS data are updated annually.

A practical guide to the American Community Survey (3-year estimates)

Transportation research. part c, emerging technologies

Transportation Networks. Optimality. Cost Functions. Deterministic User Equilibrium Assignment. Stochastic User Equilibrium Assignment. Trip Table Estimation. Network Reliability. Network Design. Conclusions. References. Index.

Transportation network analysis

This paper develops a novel linear programming formulation for autonomous intersection control (LPAIC) accounting for traffic dynamics within a connected vehicle environment. Firstly, a lane based bi-level optimization model is introduced to propagate traffic flows in the network, accounting for dynamic departure time, dynamic route choice, and autonomous intersection control in the context of system optimum network model. Then the bi-level optimization model is transformed to the linear programming formulation by relaxing the nonlinear constraints with a set of linear inequalities. One special feature of the LPAIC formulation is that the entries of the constraint matrix has only {−1, 0, 1} values. Moreover, it is proved that the constraint matrix is totally unimodular, the optimal solution exists and contains only integer values. It is also shown that the traffic flows from different lanes pass through the conflict points of the intersection safely and there are no holding flows in the solution. Three numerical case studies are conducted to demonstrate the properties and effectiveness of the LPAIC formulation to solve autonomous intersection control.

A linear programming formulation for autonomous intersection control within a dynamic traffic assignment and connected vehicle environment

Many rapidly developing countries around the world are at a crossroads when it comes to transportation, air quality, and sustainability. Indeed, the challenges presented by vehicular growth in India have motivated the search for sustainable transportation solutions. One solution constitutes ridehailing services, which are expected to reduce car ownership and provide affordable means of transportation. Another key solution is the rise of electric vehicles (EVs), which are expected to reduce greenhouse gas emission and address the growing demand for sustainable urban mobility. Using a unique survey data set collected in 2018 from a sample of 43,000 respondents spread across 20 cities in India, this paper attempts to shed light on the factors that affect adoption of on-demand transportation services and EVs in India. In particular, not only does this paper consider the socio-economic and demographic variables that affect these behavioral choices, but the modeling framework adopted in this study places a special emphasis on representing the important role played by attitudes, values, and perceptions in determining adoption of on-demand transportation services and EVs. It is observed that attitudes and values significantly affect the use of on-demand transportation services and EV ownership, suggesting that information campaigns and free trials/demonstrations would help advance the adoption of sustainable transportation modes. The model results help in the identification of policy options and infrastructure investments that can advance a sustainable transportation future in India.

Accounting for the Influence of Attitudes and Perceptions in Modeling the Adoption of Emerging Transportation Services and Technologies in India

With the growth of cars and car-sharing applications, commuters in many cities, particularly developing countries, are shifting away from public transport. These shifts have affected two key stakeholders: transit operators and first- and last-mile (FLM) services. Although most cities continue to invest heavily in bus and metro projects to make public transit attractive, ridership in these systems has often failed to reach targeted levels. FLM service providers also experience lower demand and revenues in the wake of shifts to other means of transport. Effective FLM options are required to prevent this phenomenon and make public transport attractive for commuters. One possible solution is to forge partnerships between public transport and FLM providers that offer competitive joint mobility options. Such solutions require prudent allocation of supply and optimised strategies for FLM operations and ride-sharing. To this end, we build an agent- and event-based simulation model which captures interactions between passengers and FLM services using statecharts, vehicle routing models, and other trip matching rules. An optimisation model for allocating FLM vehicles at different transit stations is proposed to reduce unserved requests. Using real-world metro transit demand data from Bengaluru, India, the effectiveness of our approach in improving FLM connectivity and quantifying the benefits of sharing trips is demonstrated. 

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An agent-based fleet management model for first- and last-mile services

Video image processing of traffic camera feeds is useful for counting and classify1 ing vehicles, estimating queue length, traffic speed and also for tracking individual 2 vehicles. Even after over three decades of research, challenges remain. Vehicle 3 detection is especially challenging when vehicles are occluded which is common 4 in heterogeneous traffic. Recently Deep Learning has shown remarkable promise 5 in solving many computer vision tasks such as object recognition, detection, and 6 tracking. We explore the promise of deep learning for vehicle detection and classifi7 cation. However, training deep learning architectures require huge labeled datasets 8 which are time-consuming and expensive to acquire. We circumvent this problem 9 by data augmentation. In particular, we show that by properly augmenting an exist10 ing large general (non-traffic) dataset with a small low-resolution heterogeneous 11 traffic dataset (that we collected) we can obtain state-of-the-art vehicle detection 12 performance. This result is expected to further encourage the wide-spread use of 13 deep learning for traffic video image processing. 14

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Vehicle Classification on Low-resolution and Occluded images: A low-cost labeled dataset for augmentation

Queue formation and dissipation are a major feature of urban road networks and estimation of queue lengths is important in traffic flow modeling. In this study, we propose a methodology for estimating the queue length at signalized intersections using only travel time data and signal timing information. We use analytical equations to trace the trajectory of the vehicle and estimate the location of the vehicle in queue. We compare the performance of our Queue Location Estimation Using Travel Time data (QLE-UTT) with another methodology which used travel time data from multiple detectors and kinematic equations for vehicle location estimation. The performances of both methodologies were found to be comparable; however, the limited data requirement and simplicity of QLE-UTT makes the methodology attractive for practical implementation. The vehicle locations estimated from QLE-UTT was used to estimate the maximum queue length in a signal cycle using a probabilistic approach. This methodology for queue length estimation was tested both on a simulated dataset, for undersaturated, saturated, and oversaturated traffic conditions, and the real-world NGSIM dataset. The methodology combining QLE-UTT and the probabilistic approach was found to estimate the vehicle trajectories and queue lengths with good accuracy.

Analytical Model for Queue Length Estimation at Signalized Intersections from Travel Time

Estimating emissions savings from alternative vehicular technologies is essential to streamline urban freight transport policy. We estimate freight emissions in the city of Chennai, India using real-world emission factors obtained from on-board emission measurement systems and trip characteristics collected through an establishment survey. India recently enforced Bharat Stage VI (BS-VI) emission standards and both the central and multiple state governments have introduced battery electric vehicle (BEV) policies. To understand potential benefits, we estimate the emission savings with BS-VI vehicles and BEVs for freight delivery. The BS-VI vehicles reduce tailpipe CO emissions by 20.3%, and HC+NOx emissions by 74.5%. The GHG emission saving in CO2 equivalents with BEVs is 50.3%, which increase with a greener energy mix, improved transmission and distribution efficiency, cleaner battery production, and more lifetime vehicle kilometres. BS-VI adoption can deliver significant emission reduction until BEVs and charging infrastructure scale up. 

Gitakrishnan Ramadurai

Papers

Accounting for the Influence of Attitudes and Perceptions in Modeling the Adoption of Emerging Transportation Services and Technologies in India

An agent-based fleet management model for first- and last-mile services

Vehicle Classification on Low-resolution and Occluded images: A low-cost labeled dataset for augmentation

Analytical Model for Queue Length Estimation at Signalized Intersections from Travel Time

Complete LCA of battery electric and conventional fuel vehicles for freight trips