We present a method of learning a Bayesian model of a traveler moving through an urban environment. This technique is novel in that it simultaneously learns a unified model of the traveler’s current mode of transportation as well as his most likely route, in an unsupervised manner. The model is implemented using particle filters and learned using Expectation-Maximization. The training data is d...

Spatial data is frequently used in urban planning contexts to support decision-making. This data can be used to plan routes through the city for public transportation or to designate zones for planning new businesses and residences. Unfortunately, few tools exist that allow non-programmers to rapidly utilize spatial data to create such plans. In this poster, we present SpaceSketch, a tool for r...

We present a method of learning a Bayesian model of a traveller moving through an urban environment. This technique is novel in that it simultaneously learns a unified model of the traveller’s current mode of transportation as well as his most likely route, in an unsupervised manner. The model is implemented using particle filters and learned using Expectation-Maximization. The training data is...

Efficient tools are needed to identify routes and schedules to evacuate affected populations to safety in face of natural disasters or terrorist attacks. Challenges arise due to violation of key assumptions (e.g. stationary ranking of alternative routes, Wardrop equilibrium) behind popular shortest path algorithms (e.g. Dijkstra’s, A*) and microscopic traffic simulators (e.g. DYNASMART). Time-e...

This paper studies parsimonious, intuitive, and effective formulas to approximate the length of Traveling Salesman Problems (TSP) and Vehicle Routing Problems (VRP). Using intuition derived from continuous models and graph theory, a formula to approximate the length of vehicle routes is proposed. In instances with different patterns of customer spatial distribution, time windows, customer deman...

This work analyzes many different models and algorithms in the literature for the optimization of routes and frequencies of buses, necessary in the framework of the support tools development to take decisions for the collective urban public transportation systems design. The problem is NP-hard, for which diverse heuristic procedures to resolve it have been proposed in the literature. The method...

The Rational Locator Hypothesis posits that individuals can, if they choose, maintain approximately steady journey-to-work travel times by adjusting their home and workplace. This hypothesis was coupled with the observation of long-term stability in drive alone journey-to-work times in metropolitan Washington (those times were unchanged from 1957 through 1968 to 1988). Despite the increase of a...

Urban mobility problems affects the vast majority of cities nowadays. Thus, systems that provide real time information to assist in planning routes and choosing the most appropriate paths are essential to make transport more effective. As an alternative solution to problems related to mobility in cities, there are the so-called Intelligent Transportation Systems (ITS) which include the Route Re...

This paper studies approximations to the average length of Vehicle Routing Problems (VRP). The approximations are valuable for strategic and planning analysis of transportation and logistics problems. The research focus is on VRP with varying number of customers, demands, and locations. This modeling environment can be used in transport and logistics models that deal with a distribution center ...

The transit network design problem (TNDP) aims to find a set of routes and corresponding frequencies for an urban public transportation system. We model the TNDP as a multi-objective combinatorial optimization problem whose resolution involves finding a Pareto front that represents different trade-off levels between opposite objectives, the travel and waiting times and the required fleet of bus...