A framework for evaluation and design of an integrated public transport system
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Contents:
The outcome is a set of DART network configurations including routes, vehicle capacities, platooning concept, timetables and standardized evaluation techniques. The developed methodologies for the design and integration of new public transport layer into the existing PT system will be investigated further.
Different DART network configurations will be created, their characteristics and impacts together with the scheduling and vehicle Platooning concept on the whole public transport system will be analysed. Moreover, an improved demand estimation for the DART service in the selected area will be conducted. First, candidate corridors are selected. This is done by conducting a qualitative and quantitative analysis of the ridership, travelling speed and line capacity of the existing public transport modes as well as future transport plans.
The public transport network is predominantly designed by transit planning agencies and authorities based on expert opinions and local service level guidelines. Timetables, rolling stock plans, maintenance planning, and crew scheduling are dominated operation schedules for the transit system. All of these components have correlations with others.
While there are some classical models which performed well in each part individually, it is still necessary and challenging to propose an integrated optimization model to consider all of these components together and provide efficient and environmental-friendly transit service for passengers. While most of the models showed excellent results in improving the schedule and operation performance, most of the applications are off-line or post-evaluation. Transit system needs a quick response to the accident and disruption. It is necessary and will be a large challenge to apply the academic models and algorithms into the real-time operation facing the interruption in the system.
Technology is transforming transportation. Bike sharing, car sharing, and rider sourcing services provided by companies such as Uber and Lyft are all shared modes, which have a strong relationship with the public transit [ , ]. The shared mobility, especially the bike sharing, could efficiently solve the last one - kilometer problem.
Jin et al. In the future, the public entities such as buses, tram, and metro should collaborate with the shared mobility modes to ensure that benefits could be widely and equitably shared.
Executive summary
This article is distributed under the terms of the Creative Commons Attribution 4. Skip to main content Skip to sections. Advertisement Hide. Download PDF. Open Access. First Online: 26 April Transit system is a complex industry including several majors and perspectives, as shown in Fig. Here is a question, how to build the smart transit system considering all the related perspectives?
The global problem is not tractable. Not only the technologies but also the transit network and service planning are needed to make the system more smart and intelligent.
A set of subproblems including traffic design problem, transit evaluation, and marketing and policy models are proposed to build the smart transit system. Open image in new window. The transit plan is the foundation for a smart urban transit system. Transit network planning problem TNP spans every decision that should be taken before the operation of the system. Due to its complexity and objectives, TNP could be divided into strategic planning network design , tactical planning frequency setting, timetabling , and operational planning vehicle scheduling, driver scheduling, and maintenance [ 8 , 9 , 10 ].
On the basis of transit planning, a smart urban transit system also required attractive marketing policy to attract more residents and also reasonable evaluating methods evaluating to improve the system. In this way, the urban transit system can be classified into these five parts which are connected and interacted with each other as shown in Fig.
Transit planning aims to find a balance between passengers benefit and operation cost [ 20 , 21 , 22 ].
Recently, based on the abundant data provided by data collection systems, it has become possible to analyze the passenger demand in various dimensions such as time-dependent demand and service reliability, which provide new aspects to design a better network. Some attempts are proposed to solve the flexible demand models [ 21 , 23 , 24 , 25 ]. For the passengers, they hope the transit network could cover a larger service area and have high accessibility [ 26 , 27 , 28 ] and fewer transfers [ 22 ].
In addition to travel demand and accessibility, the stochastic travel time [ 29 , 30 ], robustness of network [ 31 ], and multi-route transit lines [ 32 ] are taken into consideration in network design models. For most of timetable optimization problems, the objectives are to minimize the passenger waiting time or transfer time [ 50 , 51 , 52 , 53 ], despite meeting with the flexible travel demand [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 ]. Meanwhile, a major complication in transit network timetabling occurs when schedules are intended to be coordinated at a transfer stop or terminals, named timetable synchronization [ 16 , 62 , 63 ].
A special case in timetable synchronization is the first and last train organization [ 6 , 64 , 65 ].
An integrated framework
When generating the timetable, it is also important for the operators to minimize the operation cost and build up the environmentally friendly timetable [ 66 , 67 , 68 , 69 , 70 , 71 ]. For the models mentioned in those papers, most of them share same constraints, including: 1 dwell time in the station; 2 the time window of the train, which gives out the upper and lower departure time of the train in any station; and 3 train consecutive trip, which gives out the order of the first train and consecutive train.
Summarizing from the related literature, there are two research directions that have become popular in recent years: cyclic timetables and timetable recovery from disruption. Genetic algorithm No. A real bus network with 10 lines and 3 transfer nodes Guo et al.
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Although a large collection of metaheuristic application to these problems can be found in the literature, the applications are very limited to a very few numbers of classical metaheuristic such as GA and SA and none classical metaheuristics such as TS and AC. Among these methods, the GAs and SAs have been mostly used. There are limited studies that have employed a mathematical method for obtaining solutions. For example, the branch-and-bound algorithm and MIP solver in the Cplex are used to solve some mixed integer problem. Although they can provide an exact solution, they can usually be applied to some small network.
Vuchic VR Urban transit: operations, planning, and economics.
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Smart Urban Transit Systems: From Integrated Framework to Interdisciplinary Perspective
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