Deadhead Trip Minimization in City Bus Transportation: A Real Life Application

Abstract

The quality of public transportation services is one of the most important performance indicators of modern urban policies for both planning and implementation aspects. Therefore, along with the size of the city, the significance of appropriate cost evaluation and optimization of all related transportation activities increases as well. One of the most important cost factors for the public transport agencies is naturally the fuel consumption of the vehicles. In this study, the attention is focused on the metropolitan bus transport service. The specific aim is to minimize a significant portion of total fuel utilization that occurs due to the so called deadhead trip or dead mileage, which is defined as the idle distance covered by the vehicle between the garage and the route terminal stops without carrying any passengers. In this study, the results of four mathematical models for minimizing the total deadhead trip distance covered in city bus services of Izmir are presented. The models vary due to the inclusion of garage capacity restrictions or operator distinction for supporting both operational and strategical decisions. All models are applied to the recent bus schedule data, which consist of 293 routes, 1,424 buses and 10 garages, for obtaining the optimal route bus-garage allocations and garage capacities. The results of the Decentralized-Capacitated model, which is appropriate for quick implementation, promise a 7.8% reduction in total dead mileage. While on the other hand, if all garage capacities can be expanded and the bus service is maintained only by one operator as modelled in the Centralized-Uncapacitated case, even a 31.4% improvement is possible in the long term. The environmental gains as well as the financial benefits to be achieved when the solutions are actually implemented, justify the practical contribution of the study. KEY WORDSurban transport planning, deadhead trip, bus-garage allocation, garage capacity planning

Bus Fleet Management – A Systematic Literature Review

“…The purpose of BFMM realm is routing and scheduling, while also including factors that can affect productivity and quality of service: deadhead trips [38,68], idle times [23,38], dwell times due to and fuel cell technology [12,26,[63][64][65] (Figure 5). Rhetorically, retrofitting and sustainability issues further opened a chapter of infrastructure-related issues due to the unavailability of a network for recharging and refuelling of electric and fuel cell buses.…”

Good to Go? Assessing the Environmental Performance of New Mobility

“…Quantitative estimates of the relevance of deadhead travel for urban bus service suggest that the share of kilometres run empty to enable the provision of scheduled services typically range between 1% and 28%, (Adra, Michaux and André, 2004;Mahadikar, Mulagni and Sitharam, 2015;Nasibov et al, 2013). Among the limited amount of values found in literature, the lowest records were found in Bangalore, India at 1-3%, followed by Izmir, Turkey, with estimates in the 5-6% range.…”

Optimization of bus allocation to depots by minimizing dead kilometers

“…ensures that the number of buses in all depots is equal to the number of buses in the entire corporation. Constraints (5) and (6) represent domains of the variables. Dead kilometers minimization model is a mixed integer linear program (MILP) formulation and is solved by using IBM ILOG CPLEX 12.5.1 version software [14].…”

“…Researchers have employed many analytical tools to solve dead kilometers minimization problem, and some of them are discussed in the succeeding sections. Methods such as heuristic algorithms, branch and bound algorithms , and Lexicographic Goal Programming (LGP) are also used to solve dead kilometers minimization problem, which is a mixed integer program.…”

“…The problem is solved using greedy heuristic algorithm. Nasibov et al developed four models to minimize deadhead trip considering two criteria, that is, inclusion of operator distinction and garage capacities. For all models, it is assumed that route starting and ending points are at the same place.…”