The Application of Smart Phone, Weight-Mile Truck Data to Support Freight-Modeling, Performance Measures and Planning

Oregon is one of the few states that currently charge a commercial truck weight-mile tax (WMT). The Oregon Department of Transportation (ODOT) has developed a data-collection system – Truck Road Use Electronics (TRUE) – to simplify WMT collection. The TRUE system includes a smart phone application that collects and records Global Positioning System (GPS) data. The TRUE data has enormous advantages over GPS data used in previous research due to its level of geographic detail and the potential to also integrate trip origin and destination, vehicle class, and commodity-type data. This research evaluates the accuracy of the TRUE data and demonstrates its use for significant ODOT ancillary applications. Specifically, ancillary applications that address ODOT freight modeling, performance measures, and planning needs are explored. The use of the data for highly accurate trip-generation rates and mobility performance measures is demonstrated. In addition, it is shown that the TRUE data has strong potential to be used for safety, accessibility and connectivity, system condition and environmental stewardship performance measures. The potential use of the TRUE data for emissions estimates that take into account truck-type details, truck weight and detailed speed profiles is considered. Results indicate that TRUE data, integrated with ODOT weigh-in-motion (WIM) data, will greatly improve the accuracy of emission estimates at the project and regional level. This research confirms the potential use of the TRUE data for significant ancillary applications and demonstrates the regional value of the TRUE data to enhance existing freight modeling, performance measures and planning

Hiking Trip Selection Based On Reachability By Public Transport

Smart cities should enable the citizens to utilize available ressources. One of the goals is the reduction of individual motorized traffic. However, many citizens still use cars to get to the location of an ourtdoor activity. As a concrete example we use hiking trips and identify those that can be reached by public transport. The result is a map (and a corresponding GIS data set) showing the hiking locations for a single day hiking trip. The concept can be used for various other applications although there are still open questions, e.g., what part of the answer can be precomputed and what should be determined on demand

Supporting adaptive tour with high level petri nets

One of the issues for tour planning applications is to adaptively provide personalized advices for different types of tourists and tour activities. This paper proposes a high level Petri Nets based approach to providing some level of adaptation by implementing adaptive navigation in a tour node space. The new model supports dynamic reordering or removal of tour nodes along a tour path; it supports multiple travel modes and incorporates multimodality within its tour planning logic to derive adaptive tour. Examples are given to demonstrate how to realize adaptive interfaces and personalization. Future directions are also discussed at the end of this paper

An investigation of algorithms for itinerary planning.

by Lo Wai On.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 96-98).AbstractAcknowledgementsTable of ContentsList of TablesList of FiguresChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Transportation Arrangement Problem --- p.2Chapter 1.3 --- Site Planning Problem --- p.4Chapter 1.4 --- Organisation of the Thesis --- p.4Chapter Chapter 2 --- Literature Review --- p.6Chapter 2.1 --- Overview --- p.6Chapter 2.2 --- Transportation Arrangement --- p.7Chapter 2.2.1 --- A* algorithm --- p.8Chapter 2.2.2 --- A*V algorithm --- p.9Chapter 2.2.3 --- Knowledge-based approach --- p.11Chapter 2.2.4 --- ANESTA's approach --- p.13Chapter 2.3 --- Site Planning --- p.14Chapter 2.3.1 --- CICERO'S approach --- p.15Chapter 2.3.2 --- ANESTA's approach --- p.17Chapter 2.4 --- Summary --- p.19Chapter Chapter 3 --- Transportation Arrangement --- p.20Chapter 3.1 --- Overview --- p.20Chapter 3.2 --- Problem Description --- p.21Chapter 3.2.1 --- Shortest path problem --- p.21Chapter 3.2.2 --- Existing solution algorithms --- p.21Chapter 3.2.3 --- Preference consideration --- p.22Chapter 3.3 --- Zoning --- p.22Chapter 3.3.1 --- Grid-type zoning --- p.23Chapter 3.3.2 --- Density-type zoning --- p.23Chapter 3.4 --- Solution Methodology --- p.24Chapter 3.4.1 --- Data representation in the system --- p.24Chapter 3.4.2 --- Heuristic algorithm --- p.26Chapter 3.5 --- Illustrative Examples --- p.34Chapter 3.5.1 --- Example 1 - Direct Connection --- p.34Chapter 3.5.2 --- Example 2 - Three-node Path --- p.35Chapter 3.5.3 --- Example 3 - Four-node Path --- p.37Chapter 3.6 --- Computation Results --- p.38Chapter 3.6.1 --- Zoning vs. No-zoning --- p.39Chapter 3.6.2 --- Grid-type zoning vs. Density-type zoning --- p.40Chapter 3.6.3 --- Comparison between the new heuristic and the other algorithms --- p.42Chapter 3.7 --- Summary --- p.43Chapter Chapter 4 --- Site Planning --- p.45Chapter 4.1 --- Overview --- p.45Chapter 4.2 --- Problem Description --- p.46Chapter 4.2.1 --- Preference constraint --- p.46Chapter 4.2.2 --- Accessibility constraint --- p.46Chapter 4.2.3 --- Time constraint --- p.47Chapter 4.2.4 --- Problems with the ANESTA's approach --- p.47Chapter 4.3 --- Solution Methodology --- p.49Chapter 4.3.1 --- Preference handling --- p.50Chapter 4.3.2 --- Time window constraints --- p.51Chapter 4.3.3 --- Connectivity constraint --- p.57Chapter 4.3.4 --- Fitness constraint --- p.57Chapter 4.3.5 --- Travelling distance constraint --- p.58Chapter 4.3.6 --- Heuristic algorithm --- p.59Chapter 4.3.7 --- Flexibility consideration --- p.63Chapter 4.4 --- An Illustrative Example --- p.66Chapter 4.5 --- Computation Results --- p.74Chapter 4.5.1 --- Comparison of the solution quality with and without the second phase heuristic --- p.74Chapter 4.5.2 --- Investigation of the effect with the circular boundary --- p.76Chapter 4.5.3 --- Comparison with ANESTA --- p.77Chapter 4.6 --- Summary --- p.86Chapter Chapter 5 --- Conclusions --- p.88Appendix A --- p.91References --- p.9

Measuring the Value of Time in Highway Freight Transportation

This research investigated several aspects of the value of time (VOT) in the trucking industry. This included examining the marginal monetary benefits and costs of reduced and prolonged freight transportation time on highways. First, a comprehensive survey estimated truckers’ perceived VOT by combining stated preference, utility theory, conditional logit modeling, and maximum likelihood function. From the data collected around major cities in Texas and Wisconsin, the truckers’ perceived VOT was estimated to be $54.98/vehicle/hour. Second, scenario-based simulation examined urban truckload operations, the purpose of which was to examine the fleet effect of individual vehicle delay on the carrier’s operation. Two of the most congested highway segments in Houston were used for the simulation, together with constrained delivery windows. The result showed that the scenario-based vehicle VOT varied from$79.81/vehicle/hour to $120.89/vehicle/hour. Third, VOT based on commodity delay only was examined in relationship to inventory management by assuming prolonged transportation time or freight delay. Delay of chemical products was ranked as the highest VOT at$13.89/truckload/hour, followed by food products at $7.24/truckload/hour. Finally, a continuous approximation technique was developed for fleet operations in the context of less-than-truckload deliveries. The trade-offs between travel time and roadway transportation cost were derived analytically and results were used to estimate fleet value of time. Ignoring time windows, the vehicle VOT for major distribution companies in Texas was estimated to be$15.50/vehicle/hour for highway trips and $22.00/vehicle/hour for local trips. To summarize, freight VOT is not only directly due to vehicles and drivers, but depends on fleet operations and supply chain management. The several approaches adopted in this research represent possible perspectives that need to be further examined. They each reveal a component of the entire shipping process that can be appropriately utilized to calculate the overall freight VOT in future studies. For example, an urgent delivery carrying chemical products can be estimated at a total congestion cost of$162.86/vehicle/hour. However, trips with different characteristics need to be treated individually andcarefully to avoid overestimation. It remains challenging tocombineall these different elements adequately to reach valid VOT for the trucking industry