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.

An algorithm to median shortest path problem (MSPP) in the design of urban transportation networks

This paper proposes an efficient solution algorithm for realistic multi-objective median shortest path problems in the design of urban transportation networks. The proposed problem formulation and solution algorithm to median shortest path problem is based on three realistic objectives via route cost or investment cost, overall travel time of the entire network and total toll revenue. The proposed solution approach to the problem is based on the heuristic labeling and exhaustive search technique in criteria space and solution space of the algorithm respectively. The first labels each node in terms of route cost and deletes cyclic and infeasible paths in criteria space imposing cyclic break and route cost constraint respectively. The latter deletes dominated paths in terms of objectives vector in solution space in order to identify a set of Pareto optimal paths. The approach, thus, proposes a non-inferior solution set of Pareto optimal paths based on non-dominated objective vector and leaves the ultimate decision to decision-makers for purpose specific final decision during applications. A numerical experiment is conducted to test the proposed algorithm using artificial transportation network. Sensitivity analyses have shown that the proposed algorithm is advantageous and efficient over existing algorithms to find a set of Pareto optimal paths to median shortest paths problems.

Engineering a distributed infrastructure for large-scale cost-effective content dissemination over urban vehicular networks

This paper proposes a practical and cost-effective approach to construct a fully distributed roadside communication infrastructure to facilitate the localized content dissemination to vehicles in the urban area. The proposed infrastructure is composed of distributed lightweight low-cost devices called roadside buffers (RSBs), where each RSB has the limited buffer storage and is able to transmit wirelessly the cached contents to fast-moving vehicles. To enable the distributed RSBs working toward the global optimal performance (e.g., minimal average file download delays), we propose a fully distributed algorithm to determine optimally the content replication strategy at RSBs. Specifically, we first develop a generic analytical model to evaluate the download delay of files, given the probability density of file distribution at RSBs. Then, we formulate the RSB content replication process as an optimization problem and devise a fully distributed content replication scheme accordingly to enable vehicles to recommend intelligently the desirable content files to RSBs. The proposed infrastructure is designed to optimize the global network utility, which accounts for the integrated download experience of users and the download demands of files. Using extensive simulations, we validate the effectiveness of the proposed infrastructure and show that the proposed distributed protocol can approach to the optimal performance and can significantly outperform the traditional heuristics.

'Dig in' to social capital : community gardens as mechanisms for growing urban social connectedness

This article reports on research undertaken with members of a Melbourne urban community garden to explore the extent to which such a natural amenity provides opportunities for enhancing social capital. It is apparent even from this small qualitative study that membership of 'Dig In' community garden offers many benefits to its members. These benefits include increased social cohesion (the sharing of values enabling identification of common aims and the sharing of codes of behaviour governing relationships), social support (having people to turn to in times of crisis) and social connections (the development of social bonds and networks). However, the study indicates that, at least in the early stages of development, such benefits do not necessarily extend beyond the garden setting. This raises a question about the time required to develop high levels of social capital, and points to the need for further research into 'time' and 'space' aspects of community gardens.

Upgrading arc median shortest path problem for an urban transportation network

In this paper, we propose an algorithm for an upgrading arc median shortest path problem for a transportation network. The problem is to identify a set of nondominated paths that minimizes both upgrading cost and overall travel time of the entire network. These two objectives are realistic for transportation network problems, but of a conflicting and noncompensatory nature. In addition, unlike upgrading cost which is the sum of the arc costs on the path, overall travel time of the entire network cannot be expressed as a sum of arc travel times on the path. The proposed solution approach to the problem is based on heuristic labeling and exhaustive search techniques, in criteria space and solution space, respectively. The first approach labels each node in terms of upgrading cost, and deletes cyclic and infeasible paths in criteria space. The latter calculates the overall travel time of the entire network for each feasible path, deletes dominated paths on the basis of the objective vector and identifies a set of Pareto optimal paths in the solution space. The computational study, using two small-scale transportation networks, has demonstrated that the algorithm proposed herein is able to efficiently identify a set of nondominated median shortest paths, based on two conflicting and noncompensatory objectives.

Temporal variation of faecal indicator bacteria in tropical urban storm drains

Human faecal contamination poses a widespread hazard for human health. In urban areas, sewer leakage may be an important cause of faecal pollution to surface water. Faecal indicator bacteria (FIB) are the most widely used indicators to monitor surface water quality. However, assessing whether a water body is meeting water quality criteria is made difficult by the high variability of FIB concentrations over time. In this study, the variation of FIB concentration in surface water from tropical urban catchments is investigated. Eleven urban sub-catchments were sampled hourly over 24-hr and samples analysed for FIB. It was found that FIB show a diurnal pattern that is characterised by daytime FIB concentrations that are significantly higher than nighttime FIB concentrations. This observed diurnal variation of FIB closely follows that of sewer flows and contrasts with observations in rural streams where FIB concentrations are known to be low in the daytime and high during the night. Field tracer tests provide qualitative evidence of sewage exfiltration and transport to drains via preferential flow paths. The diurnal FIB variation and field tracer tests indicate the likelihood of surface water contamination due to leaking sewers. The results further suggest that contamination of surface-water drains is likely a widespread problem in tropical urban areas due to extensive drainage networks and the persistence of FIB under tropical conditions. Because of FIB variation over time, the time at which samples are collected is important in being able to capture the daily maximum and minimum FIB concentrations. The Kruskal-Wallis test shows that hourly sampling from 04:00 to 07:00 and from 12:00 to 15:00 results in significantly different FIB concentration (minimum and maximum, respectively). Furthermore, the Wilcoxon-Mann-Whitney test shows that sampling at 12:00 and 14:00 results in significantly higher FIB concentrations, while sampling at 05:00 and 04:00 or 05:00 and 06:00 results in significantly lower FIB concentrations, than sampling at other hours of the day.