An Alternative Fuel Refueling Station Location Model considering Detour Traffic Flows on a Highway Road System

With the development of alternative fuel (AF) vehicle technologies, studies on finding the potential location of AF refueling stations in transportation networks have received considerable attention. Due to the strong limited driving range, AF vehicles for long-distance intercity trips may require multiple refueling stops at different locations on the way to their destination, which makes the AF refueling station location problem more challenging. In this paper, we consider that AF vehicles requiring multiple refueling stops at different locations during their long-distance intercity trips are capable of making detours from their preplanned paths and selecting return paths that may be different from original paths for their round trips whenever AF refueling stations are not available along the preplanned paths. These options mostly need to be considered when an AF refueling infrastructure is not fully developed on a highway system. To this end, we first propose an algorithm to generate alternative paths that may provide the multiple AF refueling stops between all origin/destination (OD) vertices. Then, a new mixed-integer programming model is proposed to locate AF refueling stations within a preselected set of candidate sites on a directed transportation network by maximizing the coverage of traffic flows along multiple paths. We first test our mathematical model with the proposed algorithm on a classical 25-vertex network with 25 candidate sites through various scenarios that consider a different number of paths for each OD pair, deviation factors, and limited driving ranges of vehicles. Then, we apply our proposed model to locate liquefied natural gas refueling stations in the state of Pennsylvania considering the construction budget. Our results show that the number of alternative paths and deviation distance available significantly affect the coverage of traffic flows at the stations as well as computational time

Modeling the Feasibility and Benefits of Adopting CNG Technology in Trucks: An Application to the Greater Toronto and Hamilton Area

Heavy-duty commercial vehicles play an integral role in goods movement. Most these vehicles are powered by diesel and are high emitters of pollution in areas with high congestion due to longer travel times and idling. This is concerning from an environmental and social perspectives as diesel exhaust contributes to global warming, has negative health effects and is likely carcinogenic. The use of alternative fuels, like Compressed Natural Gas (CNG), could have the potential to counter these negative effects. However, one of the major drawbacks in fleets transitioning towards CNG is the lack of available refueling infrastructure. To overcome this obstacle, establishing a natural gas virtual pipeline in the form of a hub-and-spoke network to provide on-site refueling at truck yards via mobile refuelling tractor-trailers is proposed. A basic and transferable framework is established to determine the location of potential hubs. The estimated number of potential CNG trucks per traffic analysis zone is set as the demand to establish the market for CNG fueling. Location-allocation modeling is then used to propose optimal CNG station (i.e. hub) locations. To quantify the benefits of CNG adoption, traffic flow was predicted and EPA’s MOVES software was used to estimate emission factors for diesel heavy-duty trucks under different scenarios of CNG adoption. A Multi-Criteria Decision Analysis was then conducted to determine the potential savings associated with CNG adoption. The results from the conducted analysis suggest that CNG is a more sustainable fuel for heavy duty trucks. Further, one CNG hub is recommended for initial CNG conversion in the study area

Incorporating Driving Range Variability in Network Design for Refueling Facilities

To stimulate and facilitate the use of alternative-fuel vehicles, it is crucial to have a network of refueling or recharging stations in place that guarantees that vehicles can reach (most of) their destinations without running out of fuel. Because initial investments in these stations are restricted, it is important to choose their locations deliberately. A fast growing stream of literature therefore analyzes the problem of locating refueling or recharging stations. The models proposed in these studies assume that the driving range is fixed, although reality shows that the driving range is highly stochastic. These models thereby misrepresent the actual coverage a network of refueling stations provides to drivers. This paper introduces two problems that do take the stochastic nature of the driving range into account. We first introduce the Expected Flow Refueling Location Problem, which is to maximize the expected number of drivers who can complete their trip without running out of fuel. The Chance Constrained Flow Refueling Location Problem is to maximize the number of drivers for which the probability of running out of fuel is below a certain threshold. We prove the problems to be strongly NP-hard, propose novel mixed-integer programming formulations for these problems, and show how these models can be extended to the case that the driving range varies during a trip. Furthermore, we extensively analyze and compare our models using randomly generated problem instances and a real life case study about the Florida state highway network. Our results show that taking the stochastic nature of the driving range into account can substantially improve the network coverage, that optimal solutions are highly robust with respect to data impreciseness, and that the potential gains of stochastic models heavily depend on the driving range distribution. Based on the results, we discuss policy implications

How many fast-charging stations do we need along European highways?

For a successful market take-up of plug-in electric vehicles, fast-charging stations along the highway network play a significant role. This paper provides results from a first study on estimating the minimum number of fast-charging stations along the European highway network of selected countries (i.e., France, Germany, the Benelux countries, Switzerland, Austria, Denmark, the Czech Republic, and Poland) and gives an estimate on their future profitability. The combination of a comprehensive dataset of passenger car trips in Europe and an efficient arc-coverpath-cover flow-refueling location model allows generating results for such a comprehensive transnational highway network for the first time. Besides the minimum number of required fastcharging stations which results from the applied flow-refueling location model (FRLM), an estimation of their profitability as well as some country-specific results are also identified. According to these results the operation of fast-charging stations along the highway will be attractive in 2030 because the number of customers per day and their willingness to pay for a charge is high compared to inner-city charging stations. Their location-specific workloads as well as revenues differ significantly and a careful selection of locations is decisive for their economic operation

How the Midwest Can Lead the Hydrogen Economy: Matching Generation Assets to Distribution Markets in Planning Hydrogen Refueling Infrastructure for Trucking and Transit

Hydrogen-powered fuel cell electric vehicles provide one important path to decarbonization in transportation, particularly for heavy-duty applications such as transit and trucking. Both fleet types face a common challenge in transitioning to low emission fuels: how to economically support one-to-one replacement of conventional diesel vehicles, especially with respect to range and refueling time. This study explores the regional assets along a major freight corridor from Pittsburgh to Minneapolis that could enable a hydrogen refueling infrastructure for transit agencies and long-haul trucking, the likely early adopters of low-emission hydrogen fuel cell electric vehicles. Among the assets available along this corridor are nuclear power plants, which can be repurposed in part to generate carbon-free hydrogen. The proximity of these plants to current and future hydrogen consumers in transportation and industry could minimize delivery costs and help smooth the balance between supply and demand

How the Midwest Can Lead the Hydrogen Economy: Matching Generation Assets to Distribution Markets in Planning Hydrogen Refueling Infrastructure for Trucking and Transit

Hydrogen-powered fuel cell electric vehicles provide one important path to decarbonization in transportation, particularly for heavy-duty applications such as transit and trucking. Both fleet types face a common challenge in transitioning to low emission fuels: how to economically support one-to-one replacement of conventional diesel vehicles, especially with respect to range and refueling time. This study explores the regional assets along a major freight corridor from Pittsburgh to Minneapolis that could enable a hydrogen refueling infrastructure for transit agencies and long-haul trucking, the likely early adopters of low-emission hydrogen fuel cell electric vehicles. Among the assets available along this corridor are nuclear power plants, which can be repurposed in part to generate carbon-free hydrogen. The proximity of these plants to current and future hydrogen consumers in transportation and industry could minimize delivery costs and help smooth the balance between supply and demand

Exact solution of the evasive flow capturing problem

The Evasive Flow Capturing Problem is defined as the problem of locating a set of law enforcement facilities on the arcs of a road network to intercept unlawful vehicle flows traveling between origin-destination pairs, who in turn deviate from their route to avoid any encounter with such facilities. Such deviations are bounded by a given tolerance. We first propose a bilevel program that, in contrast to previous studies, does not require a priori route generation. We then transform this bilevel model into a single-stage equivalent model using duality theory to yield a compact formulation. We finally reformulate the problem by describing the extreme rays of the polyhedral cone of the compact formulation and by projecting out the auxiliary variables, which leads to facet-defining inequalities and a cut formulation with an exponential number of constraints. We develop a branch-and-cut algorithm for the resulting model, as well as two separation algorithms to solve the cut formulation. Through extensive experiments on real and randomly generated networks, we demonstrate that our best model and algorithm accelerate the solution process by at least two orders of magnitude compared with the best published algorithm. Furthermore, our best model significantly increases the size of the instances that can be solved optimally

A user equilibrium-based fast-charging location model considering heterogeneous vehicles in urban networks

Inappropriate deployment of charging stations not only hinders the mass adoption of Electric Vehicles (EVs) but also increases the total system costs. This paper attempts to address the problem of identifying the optimal locations of fast-charging stations in the urban network of mixed gasoline and electric vehicles with respect to the traffic equilibrium flows and the EVs' penetration. A bi-level optimization framework is proposed in which the upper level aims to locate charging stations by minimizing the total travel time and the installation costs for charging infrastructures. On the other hand, the lower-level captures re-routing behaviours of travellers with their driving ranges. A cross-entropy approach is developed to deliver the solutions with different levels of EVs' penetration. Finally, numerical studies are performed to demonstrate the fast convergence of the proposed framework and provide insights into the impact of EVs' proportion in the network and the optimal location solution on the global system cost

Modeling a potential hydrogen refueling station network for fuel cell heavy-duty vehicles in Germany in 2050

Schwerlastverkehr ist für rund acht Prozent der globalen Treibhausgasemissionen verantwortlich. Zu deren Reduzierung ist der Einsatz von Brennstoffzellen-Schwerlastfahrzeugen, welche Wasserstoff aus erneuerbaren Quellen verwenden, eine mögliche Lösung. Eine massive Verbreitung von Brennstoffzellen-Schwerlastfahrzeugen würde jedoch ein neues Wasserstoff-Tankstationsnetz erfordern und den Stromsektor beeinflussen. Diese Dissertation zielt auf die Bewertung eines potenziellen Wasserstoff-Tankstationsnetzes für Brennstoffzellen-Schwerlastfahrzeuge in Deutschland im Jahr 2050 ab. Für die Entwicklung alternativer Tankstationsnetze für Schwerlastfahrzeuge wird ein neuer Ansatz vorgestellt, welcher erforderliche Eingangsdaten generiert und ein neues Optimierungsmodell entwickelt. Die für diese Dissertation gesammelten Fahrzeug- und Infrastrukturnutzeranforderungen ermöglichen es, relevante Rahmenparameter wie Fahrzeugeffizienz, Reichweite und Tankstationsauslegung zu bestimmen. Weiterhin wird eine Analyse von mehreren tausend Schwerlastkilometern erstellt, um aktuelle Verkehrsnachfragen und -ströme zu verstehen. Anschließend ermöglicht ein Flow-Refueling-Location-Modell, erweitert um eine Standortkapazitätsbegrenzung, die Ableitung eines potentiellen Wasserstoff-Tankstationsnetzes mit den wenigsten Stationen zur Versorgung des Verkehrs. Eine Verknüpfung mit einem Open-Source-Strommodell erlaubt es, den Flexibilitätswert einer dezentralen Wasserstofferzeugung über flexibel einsetzbare Elektrolyseure für das Tankstationsnetz zu bewerten. Dass Wasserstofftankstationen für Schwerlastfahrzeuge hinsichtlich ihrer Größe sehr unterschiedlich im Vergleich zu Pkw-Stationen sind, zeigen die Ergebnisse. Die Netzwerkmodellierung resultiert in einem Wasserstofftankstationsnetz von rund 140 Stationen, welches den gesamten Schwerlastverkehr bei einer täglichen Bedarfsobergrenze von 30 Tonnen Wasserstoff pro Standort abdeckt. Dieses potenzielle Stationsnetz würde im Jahr 2050 jährliche Kosten von rund neun Milliarden Euro pro Jahr verursachen, einschließlich Betriebs- und Kapitalkosten für Stationen, Elektrolyseure und Strom. Die Kopplung dieses Tankstationsnetzes mit dem Stromnetz könnte durch eine erhöhte Flexibilität der Wasserstofferzeugung für das Stationsnetz rund eine Milliarde Euro an den genannten Ausgaben reduzieren, ebenso wie der Bau und Betrieb eines Pipelinenetzes mit zentraler Wasserstofferzeugung anstelle dezentraler Erzeugung. Insgesamt trägt diese Arbeit zu einem besseren Verständnis einer großen Schwerlastfahrzeug-Wasserstofftankinfrastruktur und deren Flexibilitätswert bei der Wasserstofferzeugung durch Kopplung mit dem Stromsektor bei

A Survey on Environmentally Friendly Vehicle Routing Problem and a Proposal of Its Classification

The growth of environmental awareness and more robust enforcement of numerous regulations to reduce greenhouse gas (GHG) emissions have directed efforts towards addressing current environmental challenges. Considering the Vehicle Routing Problem (VRP), one of the effective strategies to control greenhouse gas emissions is to convert the fossil fuel-powered fleet into Environmentally Friendly Vehicles (EFVs). Given the multitude of constraints and assumptions defined for different types of VRPs, as well as assumptions and operational constraints specific to each type of EFV, many variants of environmentally friendly VRPs (EF-VRP) have been introduced. In this paper, studies conducted on the subject of EF-VRP are reviewed, considering all the road transport EFV types and problem variants, and classifying and discussing with a single holistic vision. The aim of this paper is twofold. First, it determines a classification of EF-VRP studies based on different types of EFVs, i.e., Alternative-Fuel Vehicles (AFVs), Electric Vehicles (EVs) and Hybrid Vehicles (HVs). Second, it presents a comprehensive survey by considering each variant of the classification, technical constraints and solution methods arising in the literature. The results of this paper show that studies on EF-VRP are relatively novel and there is still room for large improvements in several areas. So, to determine future insights, for each classification of EF-VRP studies, the paper provides the literature gaps and future research needs