Solving the Median Shortest Path Problem in the Planning and Design of Urban Transportation Networks Using a Vector Labeling Algorithm

This paper proposes an alternative algorithm to solve the median shortest path problem (MSPP) in the planning and design of urban transportation networks. The proposed vector labeling algorithm is based on the labeling of each node in terms of a multiple and conflicting vector of objectives which deletes cyclic, infeasible and extreme-dominated paths in the criteria space imposing cyclic break (CB), path cost constraint (PCC) and access cost parameter (ACP) respectively. The output of the algorithm is a set of Pareto optimal paths (POP) with an objective vector from predetermined origin to destination nodes. Thus, this paper formulates an algorithm to identify a non-inferior solution set of POP based on a non-dominated set of objective vectors that leaves the ultimate decision to decision-makers. A numerical experiment is conducted using an artificial transportation network in order to validate and compare results. Sensitivity analysis has shown that the proposed algorithm is more efficient and advantageous over existing solutions in terms of computing execution time and memory space used.     

Detection Algorithms of Intentional Car Following on Smart Networks: A Primary Methodology

Abstract

This paper explores the possibility of detecting certain movements of vehicles that might provide useful information for crime investigations. It is known that existing car following models are interested in microscopic interactions between vehicles in randomly formed pairs. The present work, however, introduces the concept of macroscopic analysis of vehicle positions on a network and the idea of seeking if these movements exhibit any meaningful relationships. First of all detection algorithms are produced for two possible types of detection: (a) was a particular vehicle followed by any vehicle? and (b) did a particular vehicle follow any vehicle? These algorithms assume that every link in the network is equipped with some sort of vehicle identification or tracking device and the identities of all vehicles, such as their number plates, are fed into the program. Then a simulation program is developed to implement the first algorithm (Type (a)), as an example, to visualise the concept. Since the present paper is a preliminary and basic approach to the problem, a number of issues and details requiring further research, together with the directions which could be taken, are also identified and discussed.     

Impact of Everyday Weather on Individual Daily Travel Behaviours in Perspective: A Literature Review

Despite the hundreds of billions of dollars being spent on infrastructure development -- from roads, rail and airports to energy extraction and power networks to the Internet -- surprisingly little reliable knowledge exists about the performance of these investments in terms of actual costs, benefits and risks. This paper presents results from the first statistically significant study of cost performance in transport infrastructure projects. The sample used is the largest of its kind, covering 258 projects in 20 nations worth approximately US$90 billion (constant 1995 prices). The paper shows with overwhelming statistical significance that in terms of costs transport infrastructure projects do not perform as promised. The conclusion is tested for different project types, different geographical regions and different historical periods. Substantial cost escalation is the rule rather than the exception. For rail, average cost escalation is 45% (SD=38), for fixed links (tunnels and bridges) it is 34% (62) and for roads 20% (30). Cost escalation appears a global phenomenon, existing across 20 nations on five continents. Cost estimates have not improved and cost escalation not decreased over the past 70 years. Cost estimates used in decision-making for transport infrastructure development are highly, systematically and significantly misleading. Large cost escalations combined with large standard deviations translate into large financial risks. However, such risks are typically ignored or underplayed in decision-making, to the detriment of social and economic welfare.