Random vehicle dispatching with options and optimal fleet size

A dispatching problem with random availability of vehicles and options to send rented vehicles is considered. We assume passenger arrivals to be described by a pure-birth process. Such a problem is analytically attractive and is shown to have practical applications in vehicle dispatching models. An average cost criterion is used to determine firm's fleet size and option (renting) strategy.     

Fleet scheduling and dispatching for demand-responsive passenger services

This paper describes a software system designed to manage the deployment of a fleet of demand-responsive passenger vehicles such as taxis or variably routed buses. Multiple modes of operation are supported both for the fleet and for individual vehicles. Booking requests can be immediate (i.e. with zero notice) or in advance of travel. An initial implementation is chosen for each incoming request, subject to time-window and other constraints, and with an objective of minimising additional travel time or maximising a surrogate for future fleet capacity. This incremental insertion scheme is supplemented by post-insert improvement procedures, a periodically executed steepest-descent improvement procedure applied to the fleet as a whole, and a “rank-homing” heuristic incorporating information about future patterns of demand. A simple objective for trip-insertion and other scheduling operations is based on localised minimisation of travel time, while an alternative incorporating occupancy ratios has a more strategic orientation. Apart from its scheduling functions, the system includes automated vehicle dispatching procedures designed to achieve a favourable combination of customer service and efficiency of vehicle deployment. Provision is made for a variety of contingencies, including travel slower or faster than expected, unexpected vehicle locations, vehicle breakdowns and trip cancellations. Simulation tests indicate that the improvement procedures yield substantial efficiencies over more naı̈ve scheduling methods and that the system will be effective in real-time applications.     

Green vehicle technology to enhance the performance of a European port: A simulation model with a cost-benefit approach

In this paper, we study the impact of using a new intelligent vehicle technology on the performance and total cost of a European port, in comparison with existing vehicle systems like trucks. Intelligent autonomous vehicles (IAVs) are a new type of automated guided vehicles (AGVs) with better maneuverability and a special ability to pick up/drop off containers by themselves. To identify the most economical fleet size for each type of vehicle to satisfy the port’s performance target, and also to compare their impact on the performance/cost of container terminals, we developed a discrete-event simulation model to simulate all port activities in micro-level (low-level) details. We also developed a cost model to investigate the present values of using two types of vehicle, given the identified fleet size. Results of using the different types of vehicles are then compared based on the given performance measures such as the quay crane net moves per hour and average total discharging/loading time at berth. Besides successfully identifying the optimal fleet size for each type of vehicle, simulation results reveal two findings: first, even when not utilising their ability to pick up/drop off containers, the IAVs still have similar efficacy to regular trucks thanks to their better maneuverability. Second, enabling IAVs’ ability to pick up/drop off containers significantly improves the port performance. Given the best configuration and fleet size as identified by the simulation, we use the developed cost model to estimate the total cost needed for each type of vehicle to meet the performance target. Finally, we study the performance of the case study port with advanced real-time vehicle dispatching/scheduling and container placement strategies. This study reveals that the case study port can greatly benefit from upgrading its current vehicle dispatching/scheduling strategy to a more advanced one.     

Charging infrastructure demands of shared-use autonomous electric vehicles in urban areas

Ride-hailing is a clear initial market for autonomous electric vehicles (AEVs) because it features high vehicle utilization levels and strong incentive to cut down labor costs. An extensive and reliable network of recharging infrastructure is the prerequisite to launch a lucrative AEV ride-hailing fleet. Hence, it is necessary to estimate the charging infrastructure demands for an AEV fleet in advance. This study proposes a charging system planning framework for a shared-use AEV fleet providing ride-hailing services in urban area. We first adopt an agent-based simulation model, called BEAM, to describe the complex behaviors of both passengers and transportation systems in urban cities. BEAM simulates the driving, parking and charging behaviors of the AEV fleet with range constraints and identifies times and locations of their charging demands. Then, based on BEAM simulation outputs, we adopt a hybrid algorithm to site and size charging stations to satisfy the charging demands subject to quality of service requirements. Based on the proposed framework, we estimate the charging infrastructure demands and calculate the corresponding economics and carbon emission impacts of electrifying a ride-hailing AEV fleet in the San Francisco Bay Area. We also investigate the impacts of various AEV and charging system parameters, e.g., fleet size, vehicle battery capacity and rated power of chargers, on the ride-hailing system’s overall costs.     

Fuel cycle emissions and life cycle costs of alternative fuel vehicle policy options for the City of Houston municipal fleet

Municipal fleet vehicle purchase decisions provide a direct opportunity for cities to reduce emissions of greenhouse gases (GHG) and air pollutants. However, cities typically lack comprehensive data on total life cycle impacts of various conventional and alternative fueled vehicles (AFV) considered for fleet purchase. The City of Houston, Texas, has been a leader in incorporating hybrid electric (HEV), plug-in hybrid electric (PHEV), and battery electric (BEV) vehicles into its fleet, but has yet to adopt any natural gas-powered light-duty vehicles. The City is considering additional AFV purchases but lacks systematic analysis of emissions and costs. Using City of Houston data, we calculate total fuel cycle GHG and air pollutant emissions of additional conventional gasoline vehicles, HEVs, PHEVs, BEVs, and compressed natural gas (CNG) vehicles to the City's fleet. Analyses are conducted with the Greenhouse Gases, Regulated Emissions, and Energy use in Transportation (GREET) model. Levelized cost per kilometer is calculated for each vehicle option, incorporating initial purchase price minus residual value, plus fuel and maintenance costs. Results show that HEVs can achieve 36% lower GHG emissions with a levelized cost nearly equal to a conventional sedan. BEVs and PHEVs provide further emissions reductions, but at levelized costs 32% and 50% higher than HEVs, respectively. CNG sedans and trucks provide 11% emissions reductions, but at 25% and 63% higher levelized costs, respectively. While the results presented here are specific to conditions and vehicle options currently faced by one city, the methods deployed here are broadly applicable to informing fleet purchase decisions.     

Simulation model performance analysis of a multiple station shared vehicle system

As an alternative transportation paradigm, shared vehicle systems have become increasingly popular in recent years. Shared vehicle systems typically consist of a fleet of vehicles that are used several times each day by different users. One of the main advantages of shared vehicle systems is that they reduce the number of vehicles required to meet total travel demand. An added energy/emissions benefit comes when low-polluting (e.g., electric) vehicles are used in the system. In order to evaluate operational issues such as vehicle availability, vehicle distribution, and energy management, a unique shared vehicle system computer simulation model has been developed. As an initial case study, the model was applied to a resort community in Southern California. The simulation model has a number of input parameters that allow for the evaluation of numerous scenarios. Several measures of effectiveness have been determined and are calculated to characterize the overall system performance. For the case study, it was found that the most effective number of vehicles (in terms of satisfying customer wait time) is in the range of 3–6 vehicles per 100 trips in a 24 h day. On the other hand, if the number of relocations also is to be minimized, there should be approximately 18–24 vehicles per 100 trips. Various inputs to the model were varied to see the overall system response. The model shows that the shared vehicle system is most sensitive to the vehicle-to-trip ratio, the relocation algorithm used, and the charging scheme employed when electric vehicles are used. A preliminary cost analysis was also performed, showing that such a system can be very competitive with present transportation systems (e.g., rental cars, taxies, etc.).     

Optimal fleet conversion policy from a life cycle perspective

Vehicles typically deteriorate with accumulating mileage and emit more tailpipe air pollutants per mile. Although incentive programs for scrapping old, high-emitting vehicles have been implemented to reduce urban air pollutants and greenhouse gases, these policies may create additional sales of new vehicles as well. From a life cycle perspective, the emissions from both the additional vehicle production and scrapping need to be addressed when evaluating the benefits of scrapping older vehicles. This study explores an optimal fleet conversion policy based on mid-sized internal combustion engine vehicles in the US, defined as one that minimizes total life cycle emissions from the entire fleet of new and used vehicles. To describe vehicles' lifetime emission profiles as functions of accumulated mileage, a series of life cycle inventories characterizing environmental performance for vehicle production, use, and retirement was developed for each model year between 1981 and 2020. A simulation program is developed to investigate ideal and practical fleet conversion policies separately for three regulated pollutants (CO, NMHC, and NO_x) and for CO₂. According to the simulation results, accelerated scrapping policies are generally recommended to reduce regulated emissions, but they may increase greenhouse gases. Multi-objective analysis based on economic valuation methods was used to investigate trade-offs among emissions of different pollutants for optimal fleet conversion policies.     

Impacts of replacement of engine powered vehicles by electric vehicles on energy consumption and CO2 emissions

This paper evaluates the impacts on energy consumption and carbon dioxide (CO₂) emissions from the introduction of electric vehicles into a smart grid, as a case study. The AVL Cruise software was used to simulate two vehicles, one electric and the other engine-powered, both operating under the New European Driving Cycle (NEDC), in order to calculate carbon dioxide (CO₂) emissions, fuel consumption and energy efficiency. Available carbon dioxide data from electric power generation in Brazil were used for comparison with the simulated results. In addition, scenarios of gradual introduction of electric vehicles in a taxi fleet operating with a smart grid system in Sete Lagoas city, MG, Brazil, were made to evaluate their impacts. The results demonstrate that CO₂ emissions from the electric vehicle fleet can be from 10 to 26 times lower than that of the engine-powered vehicle fleet. In addition, the scenarios indicate that even with high factors of CO₂ emissions from energy generation, significant reductions of annual emissions are obtained with the introduction of electric vehicles in the fleet.     

Vehicle mass as a determinant of fuel consumption and secondary safety performance

One interaction between environmental and safety goals in transport is found within the vehicle fleet where fuel economy and secondary safety performance of individual vehicles impose conflicting requirements on vehicle mass from an individual’s perspective. Fleet characteristics influence the relationship between the environmental and safety outcomes of the fleet; the topic of this paper. Cross-sectional analysis of mass within the British fleet is used to estimate the partial effects of mass on the fuel consumption and secondary safety performance of vehicles. The results confirmed that fuel consumption increases as mass increases and is different for different combinations of fuel and transmission types. Additionally, increasing vehicle mass generally decreases the risk of injury to the driver of a given vehicle in the event of a crash. However, this relationship depends on the characteristics of the vehicle fleet, and in particular, is affected by changes in mass distribution within the fleet. We confirm that there is generally a trade-off in vehicle design between fuel economy and secondary safety performance imposed by mass. Cross-comparison of makes and models by model-specific effects reveal cases where this trade-off exists in other aspects of design. Although it is shown that mass imposes a trade-off in vehicle design between safety and fuel use, this does not necessarily mean that it imposes a trade-off between safety and environmental goals in the vehicle fleet as a whole because the secondary safety performance of a vehicle depends on both its own mass and the mass of the other vehicles with which it collides.     

Carrier collaboration with endogenous fleets and load factors when networks are complementary

This paper analyzes the effect of carrier collaboration on fleet capacity, fleet structures in terms of the number and the size of vehicles, and load factors. The model features complementary networks, scheduling, price elastic demands, and demand uncertainty. For the case of a given number of vehicles, the analysis shows that carrier collaboration increases vehicle sizes (thus, fleet capacity) if marginal seat costs are low while fleet capacity remains unchanged if marginal seat costs are high. If both vehicle sizes and vehicle numbers can be varied, then collaboration will always increase vehicle numbers and fleet capacity, while the effects on vehicle sizes and, thus, also load factors, are ambiguous and therewith hard to predict. Numerical simulations indicate that collaboration increases expected load factors also when the number of vehicles is endogenous.     

Siting charging stations for electric vehicle adoption in shared autonomous fleets

The transportation sector is undergoing three revolutions: shared mobility, autonomous driving, and electrification. When planning the charging infrastructure for electric vehicles, it is critical to consider the potential interactions and synergies among these three emerging systems. This study proposes a framework to optimize charging infrastructure development for increasing electric vehicle (EV) adoption in systems with different levels of autonomous vehicle adoption and ride sharing participation. The proposed model also accounts for the pre-existing charging infrastructure, vehicle queuing at the charging stations, and the trade-offs between building new charging stations and expanding existing ones with more charging ports.Using New York City (NYC) taxis as a case study, we evaluated the optimum charging station configurations for three EV adoption pathways. The pathways include EV adoption in a 1) traditional fleet (non-autonomous vehicles without ride sharing), 2) future fleet (fully autonomous vehicles with ride sharing), and 3) switch-over from traditional to future fleet. Our results show that, EV adoption in a traditional fleet requires charging infrastructure with fewer stations that each has more charging ports, compared to the future fleet which benefits from having more scattered charging stations. Charging will only reduce the service level by 2% for a future fleet with 100% EV adoption. EV adoption can reduce CO₂ emissions of NYC taxis by up to 861 Tones/day for the future fleet and 1100 Tones/day for the traditional fleet.     

An analysis of the effects of French vehicle inspection program and grant for scrappage on household vehicle transaction

The French government has implemented a periodical vehicle inspection program, which aims at maintaining proper functioning of the vehicle and ensuring the emissions control systems installed on the vehicle work properly. Also, an incentive program for scrapping old vehicles was introduced in 1994 through 1996 to promote the replacement of those vehicles with higher emissions by newer vehicles with lower emissions. A hazard-based duration model of household vehicle transaction behavior has been developed in this study to examine the effects of the inspection program and the grant for scrappage on vehicle transaction timing. The model is developed as a competing risks model assuming the following three types of competing risks: replacing one of the vehicles in the household fleet, disposing of one vehicle in the fleet, and acquiring one vehicle to add to the fleet. The empirical analysis is carried out using the panel data of French households' vehicle ownership from 1984 to 1998, obtained by the panel survey called Parc-Auto, which has been conducted by a French marketing firm, SOFRES, since 1976. The long panel observation period facilitates the introduction of macro-economic indicators into the model, enabling the analysis to distinguish the effects of policy measures from macro-economic factors. The empirical results indicate that the expected vehicle holding duration becomes 1.3 years longer under the inspection program than before the program commenced, given that the vehicle is replaced by another vehicle at the end of the holding duration; and that the conditional probability of replacing a vehicle aged 10 years and over becomes 1.2 times higher, and the average holding duration becomes shorter by 3.3 years, when the grant for scrappage is available.     

Correlation between automotive CO, HC, NO, and PM emission factors from on-road remote sensing: implications for inspection and maintenance programs

Carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide (NO) emission factors (EFs) are measured with a commercial vehicle emissions remote sensing system (VERSS) during a large-scale vehicle exhaust emissions study in Las Vegas. Particulate matter (PM) EFs are simultaneously measured for individual vehicles with a newly developed PM-VERSS based on ultraviolet backscatter light detection and ranging (Lidar). The effectiveness of CO and HC EFs as proxy for NO and PM EFs for spark-ignition vehicles is evaluated. Poor correlations were found between EFs for pollutants on an individual vehicle basis indicating that high EFs for one or more pollutants cannot be used as a predictor of high EFs for other pollutants. Stronger functional relationships became evident after averaging the EF data in bins based on rank-order of a single pollutant EF. Low overlap between the highest 10% emitters for CO, HC, NO, and PM was found. These results imply that for an effective reduction of the four pollutants, inspection and maintenance (I/M) programs, including clean screening, should measure all four pollutants individually. Fleet average CO and HC concentrations determined by gaseous VERSS were compared with fleet average CO and HC concentrations measured at low-idle and at high-idle during local I/M tests for spark-ignition vehicles. The fleet average CO concentrations measured by I/M tests at either idle were about half of those measured by remote sensing. The fleet average high-idle HC concentration measured by I/M tests was about half of that measured by VERSS while low-idle I/M and VERSS HC average concentrations were in better agreement. For a typical vehicle trip, most of the fuel is burned during non-idle conditions. I/M measurements collected during idling conditions may not be a good indicator of a vehicle’s potential to be a high emitter. VERSS measurements, when the vehicle is under a load, should more effectively identify high emitting vehicles that have a large contribution to the mobile emissions inventory.     

Optimal scheduling of public transport fleet at network level

This paper deals with the development of a strategic approach for optimizing the operation of public transport system that considers both user's objective and operator's objective. Passengers of public transport are assumed to seek a minimum wait time to conduct the trips, while on the other hand, operators are concerned with the efficient operation such as minimum fleet size. The average minimum wait time is to be achieved by creating an optimal despatching policy for each vehicle from the terminal. As for efficient operation the utilisation of vehicle should be maximised by having a minimum number of vehicles in operation. User's and operator's objectives are optimized within certain operational constraints such as vehicle capacity to maintain acceptable level of service. The i‐model is contructed in a bi‐level programming form in which the user's objective is minimized by dynamic programming and the operator's objective is minimized by various routing strategies. Furthermore, an algorithm and a contrived example are developed to solve and see the performance of the approach.     

Dynamic autonomous vehicle fleet operations: Optimization-based strategies to assign AVs to immediate traveler demand requests

Motivated by the growth of ridesourcing services and the expected advent of fully-autonomous vehicles (AVs), this paper defines, models, and compares assignment strategies for a shared-use AV mobility service (SAMS). Specifically, the paper presents the on-demand SAMS with no shared rides, defined as a fleet of AVs, controlled by a central operator, that provides direct origin-to-destination service to travelers who request rides via a mobile application and expect to be picked up within a few minutes. The underlying operational problem associated with the on-demand SAMS with no shared rides is a sequential (i.e. dynamic or time-dependent) stochastic control problem. The AV fleet operator must assign AVs to open traveler requests in real-time as traveler requests enter the system dynamically and stochastically. As there is likely no optimal policy for this sequential stochastic control problem, this paper presents and compares six AV-traveler assignment strategies (i.e. control policies). An agent-based simulation tool is employed to model the dynamic system of AVs, travelers, and the intelligent SAMS fleet operator, as well as, to compare assignment strategies across various scenarios. The results show that optimization-based AV-traveler assignment strategies, strategies that allow en-route pickup AVs to be diverted to new traveler requests, and strategies that incorporate en-route drop-off AVs in the assignment problem, reduce fleet miles and decrease traveler wait times. The more-sophisticated AV-traveler assignment strategies significantly improve operational efficiency when fleet utilization is high (e.g. during the morning or evening peak); conversely, when fleet utilization is low, simply assigning traveler requests sequentially to the nearest idle AV is comparable to more-advanced strategies. Simulation results also indicate that the spatial distribution of traveler requests significantly impacts the empty fleet miles generated by the on-demand SAMS.     

The treatment of capital costs of vehicles in evaluating road schemes

There are two ways in which new road schemes may influence capital expenditure on vehicles. Firstly, by improving utilisation of existing vehicles, the size of fleet needed to perform a given volume of work may be reduced. This will clearly reduce the amount of capital tied up in motor vehicles at any point in time, and to the extent that vehicle life is determined by age rather than mileage run, will also yield savings in terms of investment in new vehicles. Secondly, by generating additional road traffic, road schemes may lead to an increase in the stock of vehicles in use.This paper argues that the current treatment of vehicle depreciation and interest charges in U.K. cost data fails to allow correctly for either of these items. Errors of logic occur in the way in which the capital stock of vehicles is valued, and in the fact that certain overheads are ignored even when fleet size changes. Moreover, the empirical evidence supporting the current partitioning of depreciation into overhead and running cost components, and the assumption of constant hours in service after an increase in journey speed seems of doubtful validity.An alternative method of calculating vehicle capital costs, based on the concept of annual capital charge, and making explicit the assumptions with respect to vehicle utilisation, is advocated, and the sensitivity of results to the view taken of the latter is demonstrated by means of specimen calculations.     

A statistical model for estimating oxides of nitrogen emissions from light duty motor vehicles

Motor vehicle emission rate models for predicting oxides of nitrogen (NOx) emissions are insensitive to vehicle modes of operation such as cruise, acceleration, deceleration, and idle, because they are based on average trip speed. Research has shown that NOx emissions are sensitive to engine load; hence, load-based variables need to be included in emissions models. Ongoing studies attempting to incorporate these `modal' variables have experienced difficulties with: (1) incomplete and/or non-representative data sets of emissions test data vis-a-vis the modal operating profiles of the tested vehicles; (2) lack of information for predicting on-road operating parameters of vehicles; and (3) non-representative vehicles recruited for emissions tests.The objective of this research was to develop a statistical model for predicting NOx emissions from light-duty gasoline motor vehicles. The primary end use of this model is forecasting, rather than explanation of the factors that affect NOx emissions, which brings to bear different requirements from the statistical model. The three challenges noted above are addressed by: (1) analyzing a data set of more than 13 000 hot-stabilized laboratory treadmill tests on 19 driving cycles (specific speed versus time testing conditions), and 114 variables describing vehicle, engine and test cycle characteristics; (2) making the models compatible with empirical data on how vehicles are being operated in-use; and (3) developing statistical weights to account for the differences in model year distributions between the emissions testing database and the current national on-road fleets.The NOx emissions model is estimated using ordinary least-squares regression techniques, with transformed response variable and regression weights. Tree regression is employed as a tool for mining relationships among variables in the data, with particular focus on identifying useful interactions among discrete variables. Details of the model development process are presented, as well as results for the final model showing the predicted emissions algorithm for the current motor vehicle fleet in Atlanta, GA metropolitan region.     

User preferences regarding autonomous vehicles

This study gains insight into individual motivations for choosing to own and use autonomous vehicles and develops a model for autonomous vehicle long-term choice decisions. A stated preference questionnaire is distributed to 721 individuals living across Israel and North America. Based on the characteristics of their current commutes, individuals are presented with various scenarios and asked to choose the car they would use for their commute. A vehicle choice model which includes three options is estimated:

• (1)Continue to commute using a regular car that you have in your possession.
• (2)Buy and shift to commuting using a privately-owned autonomous vehicle (PAV).
• (3)Shift to using a shared-autonomous vehicle (SAV), from a fleet of on-demand cars for your commute.

A factor analysis determined five relevant latent variables describing the individuals’ attitudes: technology interest, environmental concern, enjoy driving, public transit attitude, and pro-AV sentiments. The effects that the characteristics of the individual and the autonomous vehicle have on use and acceptance are quantified through random utility models including logit kernel model taking into account panel effects.Currently, large overall hesitations towards autonomous vehicle adoption exist, with 44% of choice decisions remaining regular vehicles. Early AV adopters will likely be young, students, more educated, and spend more time in vehicles. Even if the SAV service were to be completely free, only 75% of individuals would currently be willing to use SAVs. The study also found various differences regarding the preferences of individuals in Israel and North America, namely that Israelis are overall more likely to shift to autonomous vehicles.Methods to encourage SAV use include increasing the costs for regular cars as well as educating the public about the benefits of shared autonomous vehicles.     

Congestion and emissions mitigation: A comparison of capacity,demand, and vehicle based strategies

Capacity, demand, and vehicle based emissions reduction strategies are compared for several pollutants employing aggregate US congestion and vehicle fleet condition data. We find that congestion mitigation does not inevitably lead to reduced emissions; the net effect of mitigation depends on the balance of induced travel demand and increased vehicle efficiency that in turn depend on the pollutant, congestion level, and fleet composition. In the long run, capacity-based congestion improvements within certain speed intervals can reasonably be expected to increase emissions of CO₂e, CO, and NO_x through increased vehicle travel volume. Better opportunities for emissions reductions exist for HC and PM_2.5 emissions, and on more heavily congested arterials. Advanced-efficiency vehicles with emissions rates that are less sensitive to congestion than conventional vehicles generate less emissions co-benefits from congestion mitigation.