A rising interest in electric and other alternatively fuelled vehicles of the freight transport industry is due to the emission targets and technological advancements. The purpose of this study is to ...measure the perceptions and opinions of road haulage companies on the use of electric and alternatively fuelled vans and trucks, as a part of urban logistics operations and long-haul transport in Finland. The paper is based on a case study in the form of expert interviews and a survey among road haulage companies. The paper provides valuable, new information to the companies about investing in electric and alternatively fuelled freight vehicles, and to the local authorities in cities about developing urban environment and infrastructure. As a result, this paper introduces benefits, barriers, policy recommendations and factors affecting the competitiveness of these vehicles.
This paper examines the feasibility of using electric powered vehicles in urban freight transport from a carrier's perspective, including their attitudes towards electric freight vehicles (EFVs) and ...all relevant elements affecting this business case, such as: technological features, existing restricting and promoting policies, financial and non-financial incentives, type of operations, urban settings and logistics organization. We look at the business cases for different truck sizes, varying from small vans to large trucks, in relation to the logistics requirements. This contribution combines the relevant urban freight transport solution directions: technology (both for the vehicle and the supporting IT), logistics and policy. The attitudes of the different EFVs user groups are also taken into account. Only if all these elements support each other, a feasible case can be possible at this moment. We look at the current business case and make conclusions on where it is necessary to act in the near future in order to increase an uptake of electric freight vehicles. For this analysis we use the data collected from current demonstrations that are actually running in the European FP7 project FREVUE, which includes over 100 electric-powered vehicles in the cities of Amsterdam, Lisbon, London, Madrid, Milan, Oslo, Rotterdam, and Stockholm. This data includes operational, attitudinal and financial data for the before situation in which conventional vehicles were used and for the first year(s) where electric vehicles were operated.
Freight mileages have been significantly increasing over the past decade, accounting for 11% of global greenhouse gas (GHG) emissions. Freight electrification and platooning are two promising ...technologies to mitigate the energy and environmental impacts of freight transportation. Effective coordination of charging and platooning are urgently needed to maximize the benefits of these two technologies, especially considering the current inadequate charging infrastructure, long charging duration, higher energy usage, and tight delivery schedules for long-haul electric freight vehicles (EFVs). In this study, we aim to co-optimize the platooning and charging strategies of EFVs. To achieve this goal, we developed a mixed-integer linear programming model to minimize the total system costs, including en-route charging cost, delivery delay cost, and hub charging cost. The proposed model was tested using a case study of a 595-mile Florida interstate highway route and solved by the state-of-the-art branch-and-cut algorithms, through which we demonstrated the effectiveness of using our model to identify the optimal charging and platooning schedules and quantify the spatial distribution of charging demand and platoon energy savings. We also performed sensitivity analyses to better understand the impacts of some critical factors, including the EFV models, charging station (CS) capacity, number of CSs, charging speed, and departure-time windows.
•Propose an MILP model for charging and platooning scheduling.•Platooning and charging activities complement each other.•Achieve significant reduction of energy cost.
This paper discusses the current developments, as well as the barriers and opportunities for using electric freight vehicles in daily city logistics operations based on the experiences from a number ...of running demonstrations. This paper discusses results from other studies and demonstrations that were published on electro mobility in city logistics in the last three years, as an update of an earlier state of the art review. Next, we present recent narratives based on the more than 100 electric freight vehicles (EFVs) deployed in the European project FREVUE and the experiences of TransMission in using four battery electric Cargohoppers to perform their urban deliveries in Amsterdam. Over the years the attention shifted from a focus on the limitations of EFVs in comparison to conventional vehicles, such as the limited range, towards the question how to better adapt the operations to deal with the EFV characteristics. Although, the business case for using EFVs, in comparison to conventional vehicles, is still suffering from high vehicle purchase price and uncertainty about its residual value, the use of EFVs in daily operations shows that in the majority of cases the current generation of EFVs have a good technical performance. Companies using EFVs are generally satisfied with these vehicles. Obviously still a number of barriers has to be levelled, but large scale EFV usage seems more feasible than before.
This paper studies the behavior of an energy consumption model for electric vehicles in presence of input uncertainty, that is under heterogeneous driving behaviors and traffic conditions, or at ...varied performances of technology and operating scenarios. In fact, these sources of uncertainty are often neglected in customary applications, i.e. the model is evaluated only around nominal conditions, thus inhibiting the model's ability to capture the intrinsic variability of energy consumptions observed in real driving. Therefore, a general framework to perform global and regionalized sensitivity analysis on energy consumption models is here formulated and applied to identify which model inputs contribute the most to the variance of simulated energy consumption and recovery, and which input values lead to average or extreme model outputs. Results proved that driving behaviors and traffic flow dynamics have the greatest impact on simulated consumption and show strong interaction effects with the parameters of the regenerative braking strategy with respect to the recovered energy. While the vehicle weight and the auxiliary systems have an increasing impact at lower speeds, the technology performances and the vehicle characteristics were proved to be non-influential at all. Ultimately, the model was validated against its ability to reproduce the variability of consumptions observed in field experiments. Results show a tendency to over-estimating average consumption and underestimating average recovery, i.e. the model predictions are conservative. In addition, evidence shows that there are un-modeled details of the regenerative braking strategy to be further investigated via future experimental analyses.
Travel times of freight trucks (or trucks) play a major role in trip planning, identification of efficient routes, and allocating resources for implementation of strategies. While research on travel ...time estimation models for passenger cars or traffic stream is documented in the literature, their applicability for trucks remains debatable. Truck travel is influenced by the road characteristics, surrounding land use, and demographics of an area. The focus of this research, therefore, is on estimating the truck travel times using the on-network (road) and off-network (land use and demographics) characteristics. Truck travel time data for 501 road links in North Carolina from 2019 were processed for four times of the day and two days of the week. Generalized estimating equations (GEE) were used with the average truck travel time per mile (ATTTPM) as the dependent variable. Spatial proximity (buffers of 0.25 mi, 0.50 mi, and 1 mi) and spatial weights (distance decay functions like 1/d, 1/d2, and 1/d3) were explored to check the best possible approach for capturing off-network characteristics. The office (business park and administrative areas), transportation (rest areas and parking facilities), heavy commercial, and light industrial land uses have an increasing influence on the ATTTPM. The linear model developed using data from a 0.25-mi buffer is best suited to estimate the ATTTPM. Off-peak hour delivery incentives or truck priority systems on road links near these land uses can be implemented to improve truck mobility. The methodology illustrated in this research is transferable and could be used for estimating truck travel times across a region.
Increasing freight traffic has posed greater road safety threats to Vulnerable Road Users (VRUs), including pedestrians and bicyclists, in urban communities. Although there are some disaggregated ...studies on truck-related crashes, the literature offers limited knowledge of how crash-level factors influence crashes related to urban freight. Our study uses crash data from North Carolina (2007–2019) and Tennessee (2009–2019) to explore the relationship between the crash-level factors and the occurrence and injury severity of crashes involving freight vehicles and VRUs. Crash-level factors include socio-demographics of VRUs and freight vehicle drivers, driving behaviors, temporal and weather effects, and environmental factors. The results indicate that freight-related VRU crashes, compared to nonfreight-related VRU crashes, are more likely to occur at private properties and parking areas and have a higher probability of causing severe injuries or deaths. The results also show that the involvement of larger vehicles and the occurrence at midblock segments are positively associated with more severe injury outcomes. The research results could help community and transport designers to increase attention to the safety impacts of growing freight traffic in urban communities.
•We develop a comprehensive and practical model for depot charge scheduling.•We propose methodologies to incorporate battery degradation considerations.•We perform an extensive computational ...study.•We derive several managerial insights of interest to scientists and managers.
We consider a fleet of electric freight vehicles (EFVs) that must deliver goods to a set of customers over the course of multiple days. In an urban environment, EFVs are typically charged at a central depot and rarely use public charging stations during delivery routes. Therefore, the charging schedule at the depot must be planned ahead of time so as to allow the vehicles to complete their routes at minimal cost. Vehicle fleet operators are subject to commercial electricity rate plans, which should be accounted for in order to provide an accurate estimation of the energy-related costs and restrictions. In addition, high vehicle utilization rates can accelerate battery aging, thereby requiring degradation mitigation considerations. We develop and solve a comprehensive mathematical model that incorporates a large variety of features associated with the use of EFVs. These include a realistic charging process, time-dependent energy costs, battery degradation, grid restrictions, and facility-related demand charges. Extensive numerical experiments are conducted in order to draw managerial insights regarding the impact of such features on the charging schedules of EFVs.
Although heavy-duty trucks constitute the backbone of freight transportation in the United States, they also contribute significantly to greenhouse gas emissions. Various alternative powertrains to ...reduce emissions have been assessed, but few specific to U.S. long-haul applications with a consistent basis of assumptions. To enable a more accurate assessment for all stakeholders, a representative drive cycle for long-haul truck operations in the United States is introduced (USLHC8) for modeling and simulation purposes. This was generated from 58,000 mi of real driving data through a unique random microtrip selection algorithm. USLHC8 covers a total driving time of 10 h 47 min, an average vehicle speed of 55.58 mph, and road grade ranging from −6% to +6%. To establish a benchmark for further powertrain comparisons, a vehicle-level simulation of a conventional diesel powertrain was paired with USLHC8. Benchmarks are presented for fuel consumption, well-to-wheel emissions, and total cost to society under different scenarios (present-day, mid-term, and long-term).
This paper identifies the variables related to road accidents, by means of nested Poisson and Negative Binomial models, for two urban zones. In zone 1, mixed-industrial land use predominates, while ...zone 2 is a larger area than includes zone 1 and has mainly mixed-residential land use. Zone 1 has just four road segments, zone 2 has 39 road segments.
The best nested Poisson model was selected for each zone using the Stepwise Algorithm. Given that the road accidents data have over-dispersion, also Negative Binomial models were used.
Results point out that, road accidents in zone 1 are related to BRT and buses, and inversely related to unitary freight trucks. In zone 2, road accidents are positive related with week of the day, rain, and big buses, and negatively related with articulated freight trucks. Week of the day has the highest estimated value, probably because traffic speeds are higher and traffic monitoring is lower on weekends. Results are useful to generate policies for reducing road accidents in urban mixed industrial zones. In order to improve estimations, other variables must be analyzed and others GLM must be proved.
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