Date：13th July (Thu.) 2017

Time：14:00 – 17:00

http://www.transport-titech.jp/seminar_visitor.html

Lecture 1
Title：Another Alternative to Dial’s Logit Assignment Algorithm on All Acyclic Paths
Speaker：Dr. Takeshi Nagae (Tohoku University) and Shin-ichi Inoue (The Institute of Behavioral Sciences)

Lecture 2
Title：Why does proportionality matter in traffic assignment and how to achieve it?
Speaker：Prof. Yu (Marco) Nie (Northwestern University)
Abstract for Lecture 2：
The proportionality condition has been widely used to produce a unique path flow solution in the user equilibrium traffic assignment problem.   In this talk I will first explain why proportionality offers a conceptually simple, practically viable and computationally efficient approach to determining a path flow solution that approximately conforms to the principle of entropy maximization.  I will then address two hitherto open questions: (1) whether and to what extent does the proportionality condition accord to real travel behavior; and (2)  how to develop an efficient algorithm that guarantees finding a solution to satisfy the proportionality condition strictly?  To answer the first question, we mine a large taxi trajectory data set to obtain millions of route choice observations, and uncover hundreds of valid paired alternative segments (PAS) from the data.  The results obtained by performing linear regression analysis and chi-square tests show that the majority of the PASs tested (up to 85%) satisfy the proportionality condition at a reasonable level of statistical significance.    To answer the second question, we propose a novel algorithm.  It alternates between constructing an origin-based and a destination-based bush representation of user equilibrium solutions, and iteratively solves the entropy maximization subproblem defined for each bush.  Thanks to the special structure of bushes, these subproblems can be solved efficiently. The proposed algorithm thus obviates enumerating all UE paths or collecting a set of paired alternative segments (PAS) to cover them.  We prove that the algorithm ensures convergence to a solution that perfectly satisfies the proportionality condition in general networks.   The proposed algorithm solves the problem much faster than the known alternatives, with a speedup of 3 – 8 times on large networks.
Short Bio. of Dr. Yu (Marco) Nie：
Dr. Marco Nie is currently an Associate Professor of Civil and Environmental Engineering at Northwestern University. He received his B.S. in Structural Engineering from Tsinghua University, his M.Eng. from National University of Singapore and his Ph.D. from the University of California, Davis.  Dr. Nie’s research covers a variety of topics in the areas of transportation systems analysis, transportation economics, sustainable transportation and traffic flow theory and simulation.  Dr. Nie is currently a member of TRB committee on Transportation Network Modeling (ADB30). He also serves as an Associate Editor for Transportation Science, an Area Editor for Networks and Spatial Economics, and is a member of the Editorial Advisory Board for Transportmetrica-B and Transportation Research Part B. Dr. Nie’s research has been supported by National Science Foundation, Transportation Research Board, US Department of Transportation, US Department of Energy, and Illinois Department of Transportation.

The 8th International BinN Research Seminar “Dynamic Behavior Analysis in Unsteady Networks” will be held on July 5th 2015. As keynote speakers, we will invite Dr. Konstantinos Ampountolas from University of Glasgow. Dr. Dr. Konstantinos Ampountolas is currently doing research on network analysis and in the seminar, keynote lectures would focus on functional distributional algorithm for clustering heterogeneous traffic networks using spatiotemporal data.  In addition, we discuss about new approaches of unsteady behavioral modeling with two researchers’ presentation.

Program

Ashwini Venkatasubramaniama,b,c, Ludger Eversa, and Konstantinos Ampountolas*, School of Mathematics & Statistics, Urban Big Data Centre (UBDC) http://ubdc.ac.uk University of Glasgow, UK

Title:

Functional distributional clustering of traffic networks for spatio-temporal data

Abstract:

Clustering analysis provides a selection of a finite collection of templates that well represent, in some sense, a large collection of data. Nowadays clustering has many applications in engineering, computer science, social and life sciences, due to the availability of large volumes of data from user-generated content and emerging infrastructure-based sensors. In this talk, we present a functional distributional algorithm for clustering heterogeneous traffic networks using spatiotemporal data. The proposed algorithm seeks to identify spatially contiguous clusters in Manhattan-like grid networks and has the ability to accommodate temporal data with bi-modal characteristics. The algorithm draws on a measure of distance that utilises (cumulative distribution) functions of observations rather than functions of clusters. We describe methods to determine the optimal number of clusters within a hierarchical agglomerative clustering framework. This helps to evaluate the similarity between distinct identified clusters and “true” clusters to measure the algorithm’s performance. Results demonstrate that the proposed functional distributional clustering algorithm has a greater ability to efficiently identify clusters compared to functional only and temporal only algorithms. On-going work on dynamic clustering seeks to identify clusters that change over time.

Sachiyo Fukuyama

Department of Civil Engineering, University of Tokyo

Title: Network analysis for urban planning based on the historical development process

Abstract:

We propose a method of network analysis to figure out the spatial structure and characteristics of urban districts, which are assumed to be important for efficient urban planning and renovation. We use a simple index that reflect route choice behavior for analyzing road networks in the periods before behavioral surveys started. For a case study, we apply the method to the historical networks of the old city of Barcelona and find the relation between the streets of high centrality and the placement of open spaces.

Eiji Hato and Samal Dharmarathna*

Department of Civil Engineering, University of Tokyo

*Presenter

Title:

Unsteady travel behavior under uncertainty in densified networks

Abstract:

Understanding the travellers’ behavior under uncertainty is essential to minimize the congestion and maintain the service level of densified networks during unexpected events such as earthquakes or extreme weather events. During such events, drivers’ pre-trip decisions are get disturbed and it becomes quite obvious to assume that their cognition and decision-making mechanisms are more myopic as the network condition is likely to be stochastic. But still there is some space that drivers could use their spatial knowledge on the network to choose the route.

This on-going study tries to cope with both these concepts by using the generalized recursive logit (GRL) model and compare the differences, by using the probe taxi data collected in Tokyo during the period of Great East Japan Earthquake occurred on 11th March 2011 and torrential rain occurred on 23rd July 2013. Gridlock phenomena has occurred in Tokyo for the first time, after the earthquake due to the temporary shutdown of the metropolitan expressway and all railways for checking purposes. The behavior of the sequential discount rate which generalize the drivers’ decision making dynamics and represent the degree of spatial recognition of network as a parameter is compared along with other parameters such as travel time and right turn dummy within the event by using similar data collected exactly one week before and after the earthquake respectively on 04th and 18th of March 2011. During the event of torrential rain, some of the links that has under passes and depressions were inundated and the cars or taxies couldn’t move across. Hence the travellers’ use such routes under normal circumstances had to choose alternative routes. In this case also, the aforementioned parameters were estimated and compared within the event by using the similar data collected exactly one week after the event on 30th July 2013. In addition, we would like to present the comparison of parameters between the two events as well.