The liner shipping berth scheduling problem with transit times

In this paper speed optimization of an existing liner shipping network is solved by adjusting the port berth times. The objective is to minimize fuel consumption while retaining the customer transit times including the transhipment times. To avoid too many changes to the time table, changes of port berth times are only accepted if they lead to savings above a threshold value. Since the fuel consumption of a vessel is a non-linear convex function of the speed, it is approximated by a piecewise linear function. The developed model is solved using exact methods in less than two minutes for large instances. Computational experiments on real-size liner shipping networks are presented showing that fuels savings in the magnitude 2–10% can be obtained. The work has been carried out in collaboration with Maersk Line and the tests instances are confirmed to be representative of real-life networks.     

Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization*

Improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed.     

Modeling different penetration rates of eco-driving in urban areas: Impacts on traffic flow and emissions

Climate change and air quality are two main environmental challenges in metropolitan areas. As road transportation is one of the main contributors, public administrations are facing these problems with a number of complementary policy measures: shift to cleaner modes, new fuels and vehicle technologies, demand management, and the use of information and communication technologies (ICT) applied to transportation. Eco-driving is one of the measures that present large fuel savings at individual level. Although these savings are well documented in the literature, few studies focus on how eco-drivers driving patterns affect the surrounding vehicles and the traffic in general, and more particularly what would be the impact when the number of eco-drivers grows. Using a traffic microsimulation tool, four models in urban context have been built, corresponding to the different types of urban roads. Both the base-case and the parameters setting to simulate eco-driving have been calibrated with real data collected through floating vehicles performing the trips with normal and eco behaviors. In total, 72 scenarios were simulated, varying the type of road, traffic demand, and the percentage of eco-drivers. Then, the CO₂ and NO_x emissions have been estimated through a microscopic emission model. The results show that in scenarios with low or medium demand levels and increasing number of eco-drivers, the effects are positive in terms of emissions. On the other side, with high percentage of eco-drivers and high traffic demand, the emissions rise. Higher headways and smooth acceleration and decelerations increase congestion, producing higher emissions globally.     

Resolving splits in location/allocation modeling: a heuristic procedure for transit center decisions

A heuristic procedure is developed to assign buses to transit centers (garages) in such a way that all the buses on a particular route are assigned to a single transit center. This research builds on an optimal mixed integer programming location/allocation model that splits the bus assignments when capacity limitations were reached at a transit center. The heuristic procedure adopts a two-step process: namely, assignment of all buses of a route to a unique transit center, then switching of routes to alternative transit centers to enforce capacity limitations. The procedure is shown to still provide cost savings over current locations and allocations for the Vancouver Regional Transit System (VRTS), Canada's largest urban transit network.     

Infrastructure development for alternative fuel vehicles on a highway road system

A new mathematical model for positioning alternative fuel (AF) refueling stations on directed-transportation networks with the objective of maximizing the coverage of path flow volume is proposed. This model is especially designed for developing an AF infrastructure on toll roads and other highways, where vehicles do not need to exit the road network for refueling, some candidate station locations are not located at interchanges, and some stations may only service vehicles on one driving direction. The proposed model is applied to the Pennsylvania Turnpike System using the 2011 truck traffic data and considering different vehicle driving ranges.     

Emissions,performance, and design of UK passenger vehicles

Consumer, legal, and technological factors influence the design, performance, and emissions of light-duty vehicles (LDVs). This work examines how design choices made by manufacturers for the UK market result in emissions and performance of vehicles throughout the past decade (2001–2011). LDV fuel consumption, CO₂ emissions, and performance are compared across different combinations of air and fuel delivery system using vehicle performance metrics of power density and time to accelerate from rest to 100 km/h (62 mph, t_z-62). Increased adoption of direct injection and turbocharging technologies helped reduce spark ignition (SI, gasoline vehicles) and compression ignition (CI, diesel vehicles) fuel consumption by 22% and 19%, respectively, over the decade. These improvements were largely achieved by increasing compression ratios in SI vehicles (3.6%), turbocharging CI vehicles, and engine downsizing by 5.7–6.5% across all technologies. Simultaneously, vehicle performance improved, through increased engine power density resulting in greater acceleration. Across the decade, t_z-62 fell 9.4% and engine power density increased 17% for SI vehicles. For CI vehicles, t_z-62 fell 18% while engine power density rose 28%. Greater fuel consumption reductions could have been achieved if vehicle acceleration was maintained at 2001 levels, applying drive train improvements to improved fuel economy and reduced CO₂ emissions. Fuel consumption and CO₂ emissions declined at faster rates once the European emissions standards were introduced with SI CO₂ emissions improving by 3.4 g/km/year for 2001–2007 to 7.8 g/km/year thereafter. Similarly, CI LDVs declined by 2.0 g/km/year for 2001–2007 and 6.1 g/km/year after.     

Critical aggressive acceleration values and models for fuel consumption when starting and driving a passenger car running on LPG

The models based on vehicle speed have been used to estimate fuel consumption and CO₂ emissions. However, these models could not properly estimate the change in fuel consumption and CO₂ emissions as the speed changes. As for the alternative method, people try to consider using acceleration instead of speed. Although acceleration has been seriously considered, determining critical aggressive acceleration value in relation to fuel consumption and CO₂ emissions is difficult to find. In this study, evaluation models of fuel consumption were developed using instantaneous acceleration, and we defined the critical aggressive acceleration values for different states of the vehicle from the viewpoints of fuel consumption and emissions. We used a mid-sized Liquefied Petroleum Gas (LPG) passenger car and obtained instantaneous data from a digital tachograph installed in the car while it accelerates. We developed two fuel consumption models and found critical aggressive accelerations, respectively: a model of starting vehicle that measures range of speed required to overcome the inertia during acceleration from stop state, and the other model for the driving state. We used Classification and Regression Tree (CART) analysis to find the critical aggressive accelerations at which the increments of fuel consumption change abruptly. As a result, the critical aggressive accelerations causing abrupt change in the increments of fuel consumption were found to be 2.598 m/s² for the starting of vehicles and 1.4705 m/s² when driving them. We also found that the increments of fuel consumption can be explained through quadratic and exponential functions with instantaneous acceleration.     

Optimizing the aerodynamic efficiency of intermodal freight trains

We develop an aerodynamic loading assignment model for intermodal freight trains based on an integer-programming framework to help terminal managers make up more fuel-efficient trains. This is the first use of optimization modeling to address the aerodynamics and energy efficiency of railroad intermodal trains. Several recommendations regarding railway equipment use, operations, and policy are proposed to improve fuel-efficiency and reduce emissions from intermodal transportation. Analysis of one major railroad intermodal route reveals the potential to reduce fuel consumption by 15 million gallons per year with corresponding savings of $28,000,000. Greater benefits are possible through broader implementation of the model.     

Light-duty electric vehicles in the gulf? Techno-economic assessment and policy implications

We investigate the economic and technological potential for adoption of Electric Vehicles (EVs) in Abu Dhabi through a parametric assessment, a public attitude survey, and a diffusion model. Abu Dhabi, similar to fossil-fuel-rich countries in its region, features a car-dependent society and energy subsidies while being situated in hot, desert environment challenging to EVs. We compare conventional vehicles and EVs in the region accounting for the higher use of air-conditioning (AC) in the actual climatic and driving pattern conditions of Abu Dhabi developing an EV AC consumption penalty model. Average annual AC penalty in fuel consumption over normal testing conditions is 16% for conventional vehicles and 25% for EVs based on hourly weather and vehicle utilization patterns. For 250 days per year, the expected EV range is higher than 75% of the nominal value. During the five hottest days of the recorded year, it can drop to 60% but still covering the average daily driving of the majority of users with a single charge with a 25kWh battery. With partial subsidies offered for both fuel and electricity, EV adoption impacts the state in terms of opportunity costs for these fuels in addition to environmental externalities. We calculate the state and user benefits in a parametric analysis dependent on driving distance and battery costs. If the battery cost is $325/kWh, direct and internalized benefits and costs are balanced when the EV is driven 60 km/day. A diffusion scenario of 5% by 2030 results in cumulative net savings of 6.4 million barrels of oil, a reduction of 517.000 tonnes of CO₂, and a cumulative net benefit of $127 million.     

Exploring the effectiveness of bus rapid transit a prototype agent-based model of commuting behavior

The introduction of Bus Rapid Transit (BRT), typically involving the use of exclusive bus lanes and related bus priority measures, is increasingly advocated as a flexible and cost-effective way of improving the attractiveness of public transit in congested urban areas by reducing travel times and variability. These schemes typically involve the reallocation of road space for exclusive use by buses, presenting commuters with potentially competing incentives: buses on BRT routes can run faster and more efficiently than buses running in general traffic, potentially attracting commuters to public transit and reducing congestion through modal shift from cars. However, a secondary impact may also exist; remaining car users may be presented with less congested road space, improving their journey times and simultaneously acting as an incentive for some bus-users to revert to the car. To investigate the potential for these primary and secondary impacts, we develop a prototype agent-based model to investigate the nature of these interactions and how they play out into system-wide patterns of modal share and travel times. The model allows us to test the effects of multiple assumptions about the behaviors of individual agents as they respond to different incentives introduced by BRT policy changes, such as the implementation of exclusive bus lanes, increased bus frequency, pre-boarding ticket machines and express stops, separately and together. We find that, under our assumptions, these policies can result in significant improvements in terms of individual journey times, modal shift, and length of rush hour. We see that the addition of an exclusive bus lane results in significant improvements for both car users and bus riders. Informed with appropriate empirical data relating to the behavior of individual agents, the geography and the specific policy interventions, the model has the potential to aid policymakers in examining the effectiveness of different BRT schemes, applied to broader environments.     

Automobile use,fuel economy and CO2 emissions in industrialized countries: Encouraging trends through 2008?

Car use and fuel economy are factors that determine oil demand and carbon dioxide (CO₂) emissions. Recent data on automobile utilization and fuel economy reveal surprising trends that point to changes in oil demand and CO₂ emissions. New vehicle and on-road fleet fuel economy have risen in Europe and Japan since the mid 1990s, and in the US since 2003. Combined with a plateau in per capita vehicle use in all countries analyzed, these trends indicate that per capita fuel use and resultant tail-pipe CO₂ emissions have stagnated or even declined.Fuel economy technology, while important, is not the only factor that explains changes in tested and on-road fuel economy, vehicle efficiency and transport emissions across countries. Vehicle size and performance choices by car producers and buyers, and driving distances have also played significant roles in total fuel consumption, and explain most of the differences among countries. Technology applied to new vehicles managed to drive down the fuel use per unit of horsepower or weight by 50%, yet most of the potential fuel savings were negated by overall increased power and weight, particularly in the US. Similarly, the promise of savings from dieselization of the fleet has revealed itself as a minor element of the overall improvement in new vehicle or on-road fuel economy. And the fact that diesels are driven so much more than gasoline cars, a difference that has increased since 1990, argues that those savings are minimal. This latter point is a reminder that car use, not just efficiency or fuel choice, is an important determinant of total fuel use and CO₂ emissions.We speculate that if the upward spiral of car weight and power slows or even reverses (as has been observed in Europe and Japan) and the now mandatory standards in many countries have the intended effect that fuel use will remain flat or only grow weakly for some time. If real fuel prices of 2008, which rivaled their peaks of the early 1980s, fell back somewhat but still remain well above their early 2000 values. If the prices remain high, this, combined with the strengthened fuel economy standards, may finally lead to new patterns of car ownership, use and fuel economy. However, if fuel prices continue their own stagnation or even decline after the peaks of 2008 and car use starts upward, fuel use will increase again, albeit more slowly.     

Estimating emissions based on the integration of microscopic traffic simulation and vehicle dynamics model

This study proposes an integrated simulation approach, which consists of a microscopic traffic simulation model, a vehicle dynamics model, and an emission estimation model, in order to estimate emissions based on more reliable vehicle performance measures. The vehicle performance measures such as engine power and engine speed significantly relate to the amount of emissions, and road curvatures and inclinations are the core inputs affecting these vehicle performance measures. Therefore, providing reliable vehicle performance measures reflecting the road geometric attributes is critical for a reliable emission estimation. This study proposes to use the microscopic traffic simulation model for generating vehicle trajectories, which is advantageous in modeling various traffic situations, and the vehicle dynamics model for producing the vehicle performance measures based on the vehicle trajectories. Finally, the outputs from the vehicle dynamics model are fed into the emission estimation model to compute emission measures. This study conducted a case-study using two road sections, one is a hypothesized road section, including various curvatures and inclinations with regular variations, and the other is a Kesselberg road section, which is an actual geometry in Bayern, Germany. The emission measures are estimated in these case-study road sections using both an existing simulation approach and the proposed integrated simulation approach. The difference between these two emission estimation approaches is discussed in terms of the emission measures, including fuel consumption, nitrogen oxides, and particulate matters.     

Unpacking older drivers’ mobility at roundabouts: Their visual-motor coordination through driver–vehicle–environment interactions

While mobility and safety of older drivers are challenged by age-related cognitive changes, the increasingly complex road environment has placed a higher demand on their adaptability. Older drivers experience difficulties in regulating their operational level behaviors which rely on the second-to-second decision-making, e.g., using the visuospatial information to guide their steering. The roundabout maneuver is one of the critical scenarios for older drivers which requires efficient visual and motor coordination. Understanding older drivers’ visual-motor coordination at roundabouts will provide insights into the mobility and safety of older driver population, which is important yet to be explored. This paper contributes to new measurements in driving behavior through quantitative examinations on driver–vehicle–environment interactions. The drivers’ visual-motor coordination is conceptualized as a sequence of eye fixations coupling with the vehicle trajectory in a space–time path. The experimental data were from 38 older adults’ on-road driving recorded using context and location-aware enabled eye tracking and precise vehicle movement tracking. A visual-motor coordination composite indicator (VMCCI) was developed to measure the efficiency of visual-motor coordination in GIS based on the aggregate multiple parameters of visual and motor behaviors at entry, circulating and exit stage of a roundabout. The results show that the VMCCI is a sensitive indicator for identifying risky drivers, problematic road sections, problematic behaviors. Older drivers’ VMCCI was associated with the angle of deviation at roundabouts, particularly at the entry stage. Findings of this study have implications for actual roundabout designing practice, which will contribute to improve the safety of older drivers behind the wheel.     

Driver speed compliance in Western Australia: a multivariate analysis

The importance of identifying factors affecting speeding is related to Police and insurance evidence of speeding as a contributor to road crashes, particularly those involving fatalities and serious injuries. Most of the literature on the causes of speeding regards speeding as a feature of risky driver behaviour or careless driving habits. Some of the literature also refers to the importance of vehicle and road environment features. Studies in Australia and overseas have looked at these factors and their relationship to spot speeds or average speed using bivariate analysis. Few studies have looked at the relative contribution of factors to driver speed compliance. In this study, an ordinary least squares regression procedure estimates that 62% of the variation in vehicle speeds can be explained by variables related to the road environment and vehicles. Moreover, it seems that drivers use posted speed limits as targets rather than delimiters and choose vehicle speeds based on factors other than the desire to be non-compliant. Policymakers intent on improving road safety should redress instances of mismatch between road design and posted speed limits and the possibilities of differential rules for rural/urban, day/night and weekday/weekend driving.     

Socially optimal replacement of conventional with electric vehicles for the US household fleet

In this study, a framework is proposed for minimizing the societal cost of replacing gas-powered household passenger cars with battery electric ones (BEVs). The societal cost consists of operational costs of heterogeneous driving patterns' cars, government investments for charging deployment, and monetized environmental externalities. The optimization framework determines the timeframe needed for conventional vehicles to be replaced with BEVs. It also determines the BEVs driving range during the planning timeframe, as well as the density of public chargers deployed on a linear transportation network over time. We leverage data sets that represent US household driving patterns, as well as the automobile and the energy markets, to apply the model. Results indicate that it takes 8 years for 80% of our conventional vehicle sample to be replaced with electric vehicles, under the base case scenario. The socially optimal all-electric driving range is 204 miles, with chargers placed every 172 miles on a linear corridor. All public chargers should be deployed at the beginning of the planning horizon to achieve greater savings over the years. Sensitivity analysis reveals that the timeframe for the socially optimal conversion of 80% of the sample varies from 6 to 12 years. The optimal decision variables are sensitive to battery pack and vehicle body cost, gasoline cost, the discount rate, and conventional vehicles' fuel economy. Faster conventional vehicle replacement is achieved when the gasoline cost increases, electricity cost decreases, and battery packs become cheaper over the years.     

Fuel economy gaps within and across garages: A bivariate random parameters seemingly unrelated regression approach

The key objective of this study is to investigate the interrelationship between fuel economy gaps and to quantify the differential effects of several factors on fuel economy gaps of vehicles operated by the same garage. By using a unique fuel economy database (fueleconomy.gov), users’ self-reported fuel economy estimates and government’s fuel economy ratings are analyzed for more than 7000 garages across the United States. The empirical analysis, nonetheless, is complicated owing to the presence of important methodological concerns including potential interrelationship between vehicles within the same garage and unobserved heterogeneity. To address these concerns, bivariate seemingly unrelated fixed and random parameter models are presented. With government’s test cycle ratings tending to over-estimate the actual on-road fuel economy, a significant variation is observed in the fuel economy gaps for the two vehicles across garages. A wide variety of factors such as driving style, fuel economy calculation method, and several vehicle-specific characteristics are considered. Drivers who drive for maximum gas mileage or drives with the traffic flow have greater on-road fuel economy relative to the government’s ratings. Contrarily, volatile drivers have smaller on-road fuel economy relative to the official ratings. Compared to the previous findings, our analysis suggests that the relationship between fuel type and fuel economy gaps is complex and not unidirectional. Regarding several vehicle and manufacturer related variables, the effects do not just significantly vary in magnitude but also in the direction, underscoring the importance of accounting for within-garage correlation and unobserved heterogeneity for making reliable inferences.     

Assessment of out-of-state truck activity in California

The continued growth of interstate freight trucking brings with it the potential for inaccuracies in official government statistics on trucks’ road use and pollutant emission contributions. State government agency emission estimates for states within the US commonly rely on in-state truck and fuel use records. Consequently, these records can incompletely reflect overall truck activity and emissions by neglecting the trucks that are registered and/or are fueled out of state. An in-person interview survey of 433 heavy-duty truck drivers was conducted at select points of entry into California to better understand the contribution of out-of-state trucks, their fuel use, and their emissions in California. The results indicate that non-California-registered trucks and non-California-diesel fuel make up approximately 30% of Class 8 heavy-duty truck mileage on California roads. Non-California-registered-truck mileage is disproportionately concentrated in four California air basins that have significant air quality issues.     

Substituting a tramway to a bus line in Paris: Costs and benefits

In Paris, an old bus line on the Maréchaux Boulevards has been replaced by a modern tramway. Simultaneously, the road-space has been narrowed by about a third. A survey of 1000 users of the tramway shows that the tramway hardly generated any shift from private cars towards public transit mode. However, it did generate important intra-mode transfers: from bus and subway towards tramway, and from Maréchaux boulevards towards the Périphérique (the Paris ring road) for cars. The various benefits and costs of these changes are evaluated. The welfare gains made by public transport users are more than compensated by the time losses of the motorists, and in particular, by the additional cost of road congestion on the Périphérique. The same conclusion applies with regard to CO₂ emissions: the reductions caused by the replacement of buses and the elimination of a few cars trips are less important than the increased pollution caused by the lengthening of the automobile trips and increased congestion on the ring road. Even if one ignores the initial investment of 350 M€, the social impact of the project, as measured by its net present value is negative. This is especially true for suburbanites. The inhabitants (and electors) of Paris pocket the main part of the benefits while supporting a fraction of the costs.     

内燃机车燃油单耗模块管理系统的构建与应用

内燃机车牵引总重每万吨公里所消耗的燃油量是铁路运营工作的一项重要经济指标。利用成组技术管理法与内燃机车车载油耗记录分析仪、燃油单耗模块管理软件、油品自动发放管理系统相结合,实现机车燃油单耗模块的应用,提高了机车油耗管理水平。     

On constituting cyclists as ‘hazards’

‘Hazard perception’ has become an integral part of novice driver education and training. Cyclists are often identified as one of many ‘hazards’ to look out for. We speculate that constituting cyclists as ‘hazards’, something that presents a danger or threat, may foster negative attitudes toward cyclists. Rather than accepting cyclists as ‘hazards’, our study examined the conditions that have made it possible to identify cyclists as ‘hazards’ in novice driver preparation. Informed by Michel Foucault's work on discursive practices, the analysis focused on the ‘road safety’ literature (1900–2017), the changing context in which road safety knowledge has been produced and the implications for the production of road space. This literature is important given the authority of scientific knowledge in western societies and its role in managing and governing populations. We found a shift in the middle of the twentieth century from drivers being identified as ‘hazards’ to drivers being identified as perceivers of ‘hazards’. At this time, researchers began studying drivers for their ability to recognise hazards: cyclists were routinely listed among the ‘hazards’ drivers should perceive. Out of 200 articles published on drivers' ‘hazard perception’ since the 1960s, one third categorised cyclists as ‘hazards’. Such research has informed the development and implementation of ‘hazard perception’ tests and, following Foucault, it participates in producing road space and shaping how drivers can think about themselves and other road users. While ‘scientific’ studies constitute cyclists as potential threats or sources of harm they lend authority to negative views of cyclists. We suggest ‘traffic participation’ as a more inclusive approach to driver education and training.