1,145 research outputs found
Data-Driven Optimization of Public Transit Schedule
Bus transit systems are the backbone of public transportation in the United
States. An important indicator of the quality of service in such
infrastructures is on-time performance at stops, with published transit
schedules playing an integral role governing the level of success of the
service. However there are relatively few optimization architectures leveraging
stochastic search that focus on optimizing bus timetables with the objective of
maximizing probability of bus arrivals at timepoints with delays within desired
on-time ranges. In addition to this, there is a lack of substantial research
considering monthly and seasonal variations of delay patterns integrated with
such optimization strategies. To address these,this paper makes the following
contributions to the corpus of studies on transit on-time performance
optimization: (a) an unsupervised clustering mechanism is presented which
groups months with similar seasonal delay patterns, (b) the problem is
formulated as a single-objective optimization task and a greedy algorithm, a
genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm
are employed to solve it, (c) a detailed discussion on empirical results
comparing the algorithms are provided and sensitivity analysis on
hyper-parameters of the heuristics are presented along with execution times,
which will help practitioners looking at similar problems. The analyses
conducted are insightful in the local context of improving public transit
scheduling in the Nashville metro region as well as informative from a global
perspective as an elaborate case study which builds upon the growing corpus of
empirical studies using nature-inspired approaches to transit schedule
optimization.Comment: 20 pages, 6 figures, 2 table
Bus timetable optimization model in response to the diverse and uncertain requirements of passengers for travel comfort
Most existing public transit systems have a fixed dispatching and service mode, which cannot effectively allocate resources from the perspective of the interests of all participants, resulting in resource waste and dissatisfaction. Low passenger satisfaction leads to a considerable loss of bus passengers and further reduces the income of bus operators. This study develops an optimization model for bus schedules that considers vehicle types and offers two service levels based on heterogeneous passenger demands. In this process, passenger satisfaction, bus company income, and government subsidies are considered. A bilevel model is proposed with a lower-level passenger ride simulation model and an upper-level multiobjective optimization model to maximize the interests of bus companies, passengers, and the government. To verify the effectiveness of the proposed methodology, a real-world case from Guangzhou is presented and analyzed using the nondominated sorting genetic algorithm-II (NSGA-II), and the related Pareto front is obtained. The results show that the proposed bus operation system can effectively increase the benefits for bus companies, passengers, and the governmen
Optimization Methods in Modern Transportation Systems
One of the greatest challenges in the public transportation network is the optimization of the passengers waiting time, where it is necessary to find a compromise between the satisfaction of the passengers and the requirements of the transport companies. This paper presents a detailed review of the available literature dealing with the problem of passenger transport in order to optimize the passenger waiting time at the station and to meet the requirements of companies (maximize profits or minimize cost). After a detailed discussion, the paper clarifies the most important objectives in solving a timetabling problem: the requirements and satisfaction of passengers, passenger waiting time and capacity of vehicles. At the end, the appropriate algorithms for solving the set of optimization models are presented
A short-turning policy for the management of demand disruptions in rapid transit systems
Rapid transit systems timetables are commonly designed to accommodate passenger
demand in sections with the highest passenger load. However, disruptions frequently
arise due to an increase in the demand, infrastructure incidences or as a consequence of fleet
size reductions. All these circumstances give rise to unsupplied demand at certain stations,
which generates passenger overloads in the available vehicles. The design of strategies that
guarantee reasonable user waiting time with small increases of operation costs is now an
important research topic. This paper proposes a tactical approach to determine optimal policies
for dealing with such situations. Concretely, a short-turning strategy is analysed, where
some vehicles perform short cycles in order to increase the frequency among certain stations
of the lines and to equilibrate the train occupancy level. Turn-back points should be located
and service offset should be determined with the objective of diminishing the passenger
waiting time while preserving certain level of quality of service. Computational results and
analysis for a real case study are provided.Junta de Andalucía P09-TEP-5022Natural Sciences and Engineering Research Council of Canada (NSERC) 39682-1
Electromobility in Public Transport: Scheduling of Electric Vehicles and Location Planning of the Charging Infrastructure
In recent years, considerable efforts have been made to make public transport more
environmentally friendly. This should primarily be achieved by reducing greenhouse
gas emissions. Electromobility is considered to be a key technology as electric vehicles
create a variety of benefits. However, the use of electric vehicles involves a
number of challenges. Modern battery electric vehicles have only a fractional part
of the ranges of combustion engine vehicles. Thus, a major challenge is charging the
vehicles at specific charging stations to compensate for this disadvantage. Technological
aspects of electric vehicles are also of importance and have to be considered.
Planning tasks of public transport companies are affected by these challanges, especially
vehicle scheduling. Vehicle scheduling is a well-studied optimization problem.
The objective is to cover a given set of timetabled service trips by a set of
vehicles at minimum costs. An issue strongly related to vehicle scheduling is location
planning of the charging infrastructure. For an effcient use of electric vehicles,
charging stations must be located at suitable locations in order to minimize operational
costs. Location planning of charging stations is a long-term planning task
whereas vehicle scheduling is a more short-term planning task in public transport.
This thesis examines optimization methods for scheduling electric vehicles in public
transport and location planning of the charging infrastructure. Electric vehicles'
technological aspects are particularly considered. Case studies based on real-world
data are used for evaluation of the artifacts developed. An exact optimization
method addresses scheduling of mixed vehicles fleets consisting of electric vehicles
and vehicles without range limitations. It is examined whether traditional solution
methods for vehicle scheduling are able to cope with the challenges imposed by electric
vehicles. The results show, that solution methods for vehicle scheduling are able
to deal with the additional challenges to a certain degree. However, novel methods
are required to fully deal with the requirements of electric vehicles. A heuristic
solution method for scheduling electric vehicles and models for the charging process
of batteries are developed. The impact of the detail level of electric vehicles' technological
aspects on resulting solutions is analyzed. A computational study reveales
major discrepancies between model assumptions and real charging behaviours. A
metaheuristic solution method for the simultaneous optimization of location planning
of charging stations and scheduling electric vehicles is designed to connect the
optimization problems and to open up synergy effects. In comparison to a sequential
planning, the simultaneous problem solving is necessary because a sequential
planning generally leads to either infeasible solutions or to significant increases in
costs.In den letzten Jahren wurden erhebliche Anstrengungen unternommen, um den
öffentlichen Personennahverkehr (ÖPNV) umweltfreundlicher zu gestalten. Dabei
sollen insbesondere Treibhausgasemissionen reduziert werden. Elektromobilität wird
dabei auf Grund der zahlreichen Vorteile von Elektrofahrzeugen als Schlüsseltechnologie
angesehen. Der Einsatz von Elektrofahrzeugen ist jedoch mit Herausforderungen
verbunden, da diese über weitaus geringere Reichweiten im Vergleich zu Fahrzeugen
mit Verbrennungsmotoren verfügen, weshalb ein Nachladen der Fahrzeugbatterien
während des Betriebs notwendig ist. Zudem müssen technische Aspekte von Elektrofahrzeugen, wie beispielsweise Batteriealterungsprozesse, berücksichtigt werden.
Die Fahrzeugeinsatzplanung als Teil des Planungsprozesses von Verkehrsunternehmen
im ÖPNV ist besonders von diesen Herausforderungen betroffen. Diese legt den
Fahrzeugeinsatz für die Bedienung der angebotenen Fahrplanfahrten bei Minimierung
der Gesamtkosten fest. Die Standortplanung der Ladeinfrastruktur ist eng mit
dieser Aufgabe verbunden, da für einen effizienten Einsatz der Fahrzeuge Ladestationen
an geeigneten Orten errichtet werden müssen, um Betriebskosten zu minimieren.
Die Planung der Ladeinfrastruktur ist ein langfristiges Planungsproblem, wohingegen
die Fahrzeugeinsatzplanung eine eher kurzfristige Planungsaufgabe darstellt.
Diese Dissertation befasst sich mit Optimierungsmethoden für die Fahrzeugeinsatzplanung
mit Elektrofahrzeugen und mit der Standortplanung der Ladeinfrastruktur.
Technische Aspekte von Elektrofahrzeugen werden dabei berücksichtigt.
Die entwickelten Artefakte werden mit Hilfe von realen Datensätzen evaluiert. Durch
eine exakte Optimierungsmethode für die Fahrzeugeinsatzplanung mit gemischten
Fahrzeugflotten bestehend aus Fahrzeugen mit und ohne Reichweiterestriktionen
wird die Anwendbarkeit von Optimierungsmethoden ohne Berücksichtigung von
Reichweitebeschränkungen auf die Herausforderungen von Elektrofahrzeugen untersucht.
Die Ergebnisse zeigen, dass herkömmliche Optimierungsmethoden für die
neuen Herausforderungen bis zu einem gewissen Grad geeignet sind, es jedoch neuartige
Lösungsmethoden erfordert, um den Anforderungen von Elektrofahrzeugen
vollständig gerecht zu werden. Mit Hilfe einer heuristischen Lösungsmethode für
die Fahrzeugeinsatzplanung mit Elektrofahrzeugen und Modellen für den Ladeprozess
von Batterien wird untersucht, inwiefern sich der Detailgrad bei der Abbildung
von Ladeprozessen auf resultierende Lösungen auswirkt. Erhebliche Unterschiede
zwischen Modellannahmen und realen Gegebenheiten von Ladeprozessen werden
herausgearbeitet. Durch ein metaheuristisches Lösungsverfahren für die simultane
Optimierung der Standortplanung der Ladeinfrastruktur und der Fahrzeugeinsatzplanung
werden beide Problemstellungen miteinander verbunden, um Synergieeffekte
offenzulegen. Im Vergleich zu einer sequentiellen Planung ist ein simultanes Lösen
notwendig, da ein sequentielles Lösen entweder zu unzulässigen Ergebnissen oder zu
erheblichen Kostensteigerungen führt
Optimization Models for Improving Bus Transit Services
To provide efficient public transportation services in areas with high demand variability over time, it may be desirable to switch vehicles between different types of services such as conventional services (with fixed routes and schedules) for high demand periods and flexible route services during low demand periods. Thus, this dissertation analyzes and compares conventional, flexible, and variable type bus service alternatives. Optimization formulations and numerical results show how the demand variability over time and other factors affect the relative effectiveness of such services. A model for connecting one terminal and one local region is solved with analytic optimization. Then, models are extended to consider multiple regions as well as multiple periods. Numerical results of problems for multiple regions and multiple periods are also discussed.
Secondly, a problem of integration of bus transit services (i.e., conventional and flexible services) with mixed fleets of buses is explored. A hybrid method combining a genetic algorithm and analytic optimization is used. Numerical analyses confirm that the total system cost can be reduced by integrating bus services with mixed fleets and switching service types and vehicles over time among regions in order to better fit actual demand densities. The solution optimality and the sensitivity of results to several important parameters are also explored.
Thirdly, transit ridership may be sensitive to fares, travel times, waiting times, and access times. Thus, elastic demands are considered in the formulations to maximize the system welfare for conventional and flexible services. Numerical examples find that with the input parameters assumed here, conventional services produce greater system welfare (consumer surplus + producer surplus) than flexible services. Numerical analysis finds that conventional and flexible services produce quite acceptable trips with the zero subsidies, compared to various financially constrained (subsidized) cases. For both conventional and flexible services, it is also found that total actual trips increase as subsidies increase. When the cost is fully subsidized, conventional services produce 79.2% of potential trips and flexible services produce 81.9% of potential trips.
Several methods are applied to find solutions for nonlinear mixed integer formulations. Their advantages and disadvantages are also discussed in the conclusions section
A GRASP approach for solving large scale electric bus scheduling problems
Electrifying public bus transportation is a critical step in reaching net-zero goals. In this paper, the focus is on the problem of optimal scheduling of an electric bus (EB) fleet to cover a public transport timetable. The problem is modelled using a mixed integer program (MIP) in which the charging time of an EB is pertinent to the battery’s state-of-charge level. To be able to solve large problem instances corresponding to real-world applications of the model, a metaheuristic approach is investigated. To be more precise, a greedy randomized adaptive search procedure (GRASP) algorithm is developed and its performance is evaluated against optimal solutions acquired using the MIP. The GRASP algorithm is used for case studies on several public transport systems having various properties and sizes. The analysis focuses on the relation between EB ranges (battery capacity) and required charging rates (in kW) on the size of the fleet needed to cover a public transport timetable. The results of the conducted computational experiments indicate that an increase in infrastructure investment through high speed chargers can significantly decrease the size of the necessary fleets. The results also show that high speed chargers have a more significant impact than an increase in battery sizes of the EBs
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