Extending TRANSIMS Technology to an Integrated Multilevel Representation

The TRANSIMS system developed at Los Alamos in the USA over the past decade is a world leader in providing an integrated land-use transportation dynamical model for large areas with a million or more inhabitants. TRANSIMS uses standard survey data to create synthetic micropopulations, including family structure, to simulate trip making and emergent traffic dynamics. We propose to extend TRANSIMS by adapting it to a new multi-level representation, allowing dynamics to be algebraically integrated at the micro-, meso- and macro-levels. The new representation builds a lattice hierarchy in a way that integrates non-partitional hierarchies of links and routes based on the usual hierarchy of geographical zones, e.g. neighbourhoods, districts, cities, counties and countries. Applying the representation to a big city starts by defining sets of zones at different levels. At the first level, N, is the street. This can be subdivided to building plots at level N-1, buildings at level N-2, and even rooms at level N-3. At level N+1 are the neighbourhoods, at level N+2 is the set of district zones (each of them containing the different neighbourhoods in the previous level), and at the top level N+3 (in this case), is just one zone, the city itself. If a larger study area is to be considered, we would have a whole set of N+3 zones defining N+4-level areas, and so on, extending to the level of counties, countries or even continents. This paper will explain the fundamentals of TRANSIMS technology and compare it to other systems. We will show how TRANSIMS and the new multi-level representation can be brought together to give new insights into the macro-dynamics of very large road systems such as London, England and even the whole of Europe

Dynamic Time-Dependent Route Planning in Road Networks with User Preferences

There has been tremendous progress in algorithmic methods for computing driving directions on road networks. Most of that work focuses on time-independent route planning, where it is assumed that the cost on each arc is constant per query. In practice, the current traffic situation significantly influences the travel time on large parts of the road network, and it changes over the day. One can distinguish between traffic congestion that can be predicted using historical traffic data, and congestion due to unpredictable events, e.g., accidents. In this work, we study the \emph{dynamic and time-dependent} route planning problem, which takes both prediction (based on historical data) and live traffic into account. To this end, we propose a practical algorithm that, while robust to user preferences, is able to integrate global changes of the time-dependent metric~(e.g., due to traffic updates or user restrictions) faster than previous approaches, while allowing subsequent queries that enable interactive applications

A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data

The increased availability of large-scale trajectory data around the world provides rich information for the study of urban dynamics. For example, New York City Taxi Limousine Commission regularly releases source-destination information about trips in the taxis they regulate. Taxi data provide information about traffic patterns, and thus enable the study of urban flow -- what will traffic between two locations look like at a certain date and time in the future? Existing big data methods try to outdo each other in terms of complexity and algorithmic sophistication. In the spirit of "big data beats algorithms", we present a very simple baseline which outperforms state-of-the-art approaches, including Bing Maps and Baidu Maps (whose APIs permit large scale experimentation). Such a travel time estimation baseline has several important uses, such as navigation (fast travel time estimates can serve as approximate heuristics for A search variants for path finding) and trip planning (which uses operating hours for popular destinations along with travel time estimates to create an itinerary).Comment: 12 page

Exploring travellers’ risk preferences with regard to travel time reliability on the basis of GPS trip records

Travel time reliability has attracted considerable interest in the field of route choice modelling. Knowing how individuals choose paths with uncertain travel times is fundamental to advancing our understanding of route choice behaviour and thus driving the development of route guidance systems. In general, existing navigation systems provide the shortest path on the basis of distance or travel time, even though many travellers do not intend to choose the shortest path. Several studies have shown that the probability of delay or travel time reliability is an important factor in a traveller’s route choice decision. Learning a traveller’s risk preference with regard to travel time reliability is important for designing a preferable route. Traditionally, route choice data for individual preference analysis are collected by conducting stated preference surveys. However, this approach is difficult to avoid its inherent limitation, namely a lack of honest, accurate, and bias-free reporting. To overcome these problems, the present study proposes a new data collection methodology that facilitates estimation of a traveller’s risk preference on the basis of large-scale GPS trip records. The lower and upper bounds of individual risk preference can be estimated by exhausting a series of reliable paths with different on-time arrival probabilities and using the theory of stochastic dominance. Then, a regression model based on a logistic function is established to explore how socio-demographic and trip characteristics influence the lower and upper bounds. Thus, individual properties, such as age, and pre-trip information, such origindestination (OD) distance, departure time, and day of week, are found to have a significant influence on the degree of risk preference

HIGHWAY ALIGNMENT ALONG THE CORRIDORS USING REMOTE SENSING AND GEOGRAPHICAL INFORMATION SYSTEM - PERUNDURAI TO PALANI, TAMILNADU, INDIA

Best route location and highway alignment selection process is a complicated one due to many variables it must be considered. Geographic Information Systems (GIS) can easily represent such variables, including topography, environment, built-up areas and geology variables. It is to identify the short route for the vehicles travelling from Perundurai to Palani and to diminish the time journey for the vehicles with possible routes for laying eco-friendly highway. This study took compensation of GIS capabilities that present the ability to overlay maps, merge them and execute spatial analysis on different layers of information in either two or three dimensions. GIS model for route location and highway alignment developed and worn to create alternate highway route applications. After the alternatives are preliminarily deliberated using ArcGIS9.3, the imitation is used to analyze, evaluate and to select the best alternative with least impacts on environment and economy. The selected highway is supposed to connect three districts viz. Erode, Tirupur and Dindugal. In final stages of examination and assessment, the replica envelops the high capabilities in analyzing the impacts of every alternatives, with buffering and spatial relations.   Three different routes are identified as left, middle and right routes. Right route is identified as best route which fulfils least cost with eco-friendly environment, material reduction on number of bridges and culverts