Assessing air quality and health benefits of the Clean Truck Program in the Alameda corridor,CA

In this paper, vehicle microscopic simulation and emission models were combined with an air pollutant dispersion model and a health assessment tool to quantify some social costs resulting from urban freight transportation in the Alameda corridor that links the Ports of Los Angeles and Long Beach to downtown Los Angeles. Traffic on two busy freeways, the I-710 and the I-110, and some heavily trafficked arterial roads was analyzed to estimate the health impacts caused by drayage truck emissions of particulate matter (PM) for four different years: 2005, which serves as a baseline for various pollution inventories, as well as 2008, 2010 and 2012. These years correspond to deadlines for the Clean Truck Program (CTP), which was put in place to improve air quality in the Alameda corridor. Results show that the health costs from particulate matter (PM) emitted by drayage trucks exceeded 440 million dollars in 2005. However, these costs decreased by 36%, 90%, and 96% after accounting for the requirements of the 2008, 2010, and 2012 CTP deadlines. These results quantify the magnitude of the social costs generated by drayage trucks in the Alameda corridor, suggest that these costs justified replacing drayage trucks operating there, and indicate that the Clean Truck Program likely exceeded its target.     

Characteristics of gaseous and particulate pollutants exhaust from logistics transportation vehicle on real-world conditions

On-road vehicle tests of three heavy duty diesel trucks were conducted by a portable emission measurement system (PEMS) in Chengdu, China. SEMTECH-ECOSTAR provided by Sensors Inc. was employed to detect gaseous emissions and MI2, an emissions measuring instrument powered by the Pegasor Particulate Sensor (PPS) was used to detect particulate emissions during the tests. The impacts of speed, acceleration and engine load on emissions were analyzed. The average nitrogen oxides (NOx) emission factors of the heavy duty diesel truck (HDDT), medium-duty diesel truck (MDDT), light duty diesel truck (LDDT) were 7.29, 5.29 and 5.53 g/km. The particulate emission factors were 0.60, 0.30 and 0.14 g/km respectively, higher than the similar reported in the previous studies. Both gaseous and particulate emission exhibit significant correlations with the change in vehicle speed, acceleration and power demand. The highest emission was generally in high VSPs and higher loads. High engine load caused by aggressive driving was the main factor of high emissions for the vehicles on real-world conditions.     

Fuel price elasticities for single-unit truck operations in the United States

This paper provides fuel price elasticity estimates for single-unit truck activity, where single-unit trucks are defined as vehicles on a single frame with either (1) at least two axles and six tires; or (2) a gross vehicle weight greater than 10,000 lb. Using data from 1980 to 2012, this paper applies first-difference and error correction models and finds that single-unit truck activity is sensitive to certain macroeconomic and infrastructure factors (gross domestic product, lane miles expansion, and housing construction), but is not sensitive to diesel fuel prices. These results suggest that fuel price elasticities of single unit truck activity are inelastic. These results may be used by policymakers in considering policies that have a direct impact on fuel prices, or policies whose effects may be equivalent to fuel price adjustments.     

Vehicle reidentification and travel time measurement on congested freeways

The paper presents an algorithm for matching individual vehicles measured at a freeway detector with the vehicles’ corresponding measurements taken earlier at another detector located upstream. Although this algorithm is potentially compatible with many vehicle detector technologies, the paper illustrates the method using existing dual-loop detectors to measure vehicle lengths. This detector technology has seen widespread deployment for velocity measurement. Since the detectors were not developed to measure vehicle length, these measurements can include significant errors. To overcome this problem, the algorithm exploits drivers’ tendencies to retain their positions within dense platoons. The otherwise complicated task of vehicle reidentification is carried out by matching these platoons rather than individual vehicles. Of course once a vehicle has been matched across neighboring detector stations, the difference in its arrival time at each station defines the vehicle’s travel time on the intervening segment.Findings from an application of the algorithm over a 1/3 mile long segment are presented herein and they indicate that a sufficient number of vehicles can be matched for the purpose of traffic surveillance. As such, the algorithm extracts travel time data without requiring the deployment of new detector technologies. In addition to the immediate impacts on traffic monitoring, the work provides a means to quantify the potential benefits of emerging detector technologies that promise to extract more detailed information from individual vehicles.     

Age related annual crash incidence rate ratios in professional drivers of heavy goods vehicles

ObjectivesEvidence concerning crash risk for older heavy vehicle drivers is sparse, making it difficult to assess if it is prudent to encourage older drivers to remain in the workforce in a climate of labour shortages. The objective of this study was to estimate annual crash rate ratios of older male heavy vehicle drivers relative to their middle aged peers.MethodsData utilized in this study includes all crashes meeting inclusion criteria involving heavy goods vehicles, categorised as rigid trucks and articulated trucks; this data was recorded by the New South Wales Roads and Traffic Authority. The exposure to the risk of a crash was represented by distance travelled for each vehicle type and year, by age of driver, as estimated by the Australian Survey of Motor Vehicle Use. Negative binomial regression modelling was applied to estimate annual crash incidence rate ratios for male drivers in various age groups.ResultsA total of 26,146 crashes occurred in New South Wales during 1999–2006, involving a total of 54,191 vehicles; removing observations that did not meet the inclusion criteria, 19,736 observations remained representing 12,501 crashes. For rigid trucks, the incidence rate ratio for drivers aged 65+ years, compared to 45–54 year olds, was 0.74 (95% CI 0.51, 0.98). For articulated trucks, the annual crash incidence rate ratio for drivers aged 65+ years compared to 45–54 year olds was 1.4 (95% CI 0.96, 1.9), and that for drivers aged 55–64 years compared to 45–54 year olds was 1.1 (95% CI 0.83, 1.3).ConclusionsOlder male professional drivers of heavy goods vehicles have lower risk of crashes in rigid vehicles, possibly due to accrued driving experience and self-selection of healthy individuals remaining in the workforce. Thus, encouraging these drivers to remain in the workforce is appropriate in the climate of labour shortages, as this study provides evidence that to do so would not endanger road safety.     

Classifying the purpose of stopped truck events: An application of entropy to GPS data

This paper applies the concept of entropy to mine large volumes of global positioning system (GPS) data in order to determine the purpose of stopped truck events. Typical GPS data does not provide detailed activity information for a given stop or vehicle movement. We categorize stop events into two types: (1) primary stops where goods are transferred and (2) secondary stops where vehicle and driver needs are met, such as rest stations. The proposed entropy technique measures the diversity of truck carriers with trucks that dwell for 15 min or longer at a given location. Larger entropy arises from a greater variety of carriers and an even distribution of stop events among these carriers. An analysis confirms our initial hypothesis that the stop locations used for secondary purposes such as fuel refills and rest breaks tend to have higher entropy, reflecting the diversity of trucks and carriers that use these facilities. Conversely, primary shipping depots and other locations where goods are transferred tend to have lower entropy due to the lower variety of carriers that utilize such locations.     

Length-based vehicle classification using event-based loop detector data

Length-based vehicle classification is an important topic in traffic engineering, because estimation of traffic speed from single loop detectors usually requires the knowledge of vehicle length. In this paper, we present an algorithm that can classify vehicles passing by a loop detector into two categories: long vehicles and regular cars. The proposed algorithm takes advantage of event-based loop detector data that contains every vehicle detector actuation and de-actuation “event”, therefore time gaps between consecutive vehicles and detector occupation time for each vehicle can be easily derived. The proposed algorithm is based on an intuitive observation that, for a vehicle platoon, longer vehicles in the platoon will have relatively longer detector occupation time. Therefore, we can identify longer vehicles by examining the changes of occupation time in a vehicle platoon. The method was tested using the event-based data collected from Trunk Highway 55 in Minnesota, which is a high speed arterial corridor controlled by semi-actuated coordinated traffic signals. The result shows that the proposed method can correctly classify most of the vehicles passing by a single loop detector.     

Eco-driving key factors that influence fuel consumption in heavy-truck fleets: A Colombian case

This research identifies key variables that influence fuel consumption that might be improved through eco-driving training programs under three circumstances that have been scarcely studied before: (a) heavy- and medium-duty truck fleets, (b) long-distance freight transport, and (c) the Latin American region. Based on statistical analyses that include multivariate regression of operational variables on fuel consumption, the impacts of an eco-driving training campaign were measured by comparing ex ante and ex post data. Operational variables are grouped into driving errors, trip conditions, driver behavior, driver profile, and vehicle attributes.The methodology is applied in a freight fleet with nationwide transport operations located in Colombia, where the steepness of its roads plays an important role in fuel consumption. The fleet, composed of 18 trucks, is equipped with state-of-the-art real-time data logger systems. During four months, 517 trips traveling a total distance of 292,512 km and carrying a total of 10,034 tons were analyzed.The results show a baseline average fuel consumption (FC) of 1.716 liters per ton-100 km. A different logistics performance indicator, which measures FC in liters per ton transported each 100 km, shows an average of 3.115. After the eco-driving campaign, reductions of 6.8% and 5.5% were obtained. Drivers’ experience, driving errors, average speed, and weight-capacity ratio, among others, were found to be highly relevant to FC. In particular, driving errors such as acceleration, braking and speed excesses are the most sensitive to eco-driving training, showing reductions of up to 96% on the average number of events per trip.     

Current map-matching algorithms for transport applications: State-of-the art and future research directions

Map-matching algorithms integrate positioning data with spatial road network data (roadway centrelines) to identify the correct link on which a vehicle is travelling and to determine the location of a vehicle on a link. A map-matching algorithm could be used as a key component to improve the performance of systems that support the navigation function of intelligent transport systems (ITS). The required horizontal positioning accuracy of such ITS applications is in the range of 1 m to 40 m (95%) with relatively stringent requirements placed on integrity (quality), continuity and system availability. A number of map-matching algorithms have been developed by researchers around the world using different techniques such as topological analysis of spatial road network data, probabilistic theory, Kalman filter, fuzzy logic, and belief theory. The performances of these algorithms have improved over the years due to the application of advanced techniques in the map matching processes and improvements in the quality of both positioning and spatial road network data. However, these algorithms are not always capable of supporting ITS applications with high required navigation performance, especially in difficult and complex environments such as dense urban areas. This suggests that research should be directed at identifying any constraints and limitations of existing map matching algorithms as a prerequisite for the formulation of algorithm improvements. The objectives of this paper are thus to uncover the constraints and limitations by an in-depth literature review and to recommend ideas to address them. This paper also highlights the potential impacts of the forthcoming European Galileo system and the European Geostationary Overlay Service (EGNOS) on the performance of map matching algorithms. Although not addressed in detail, the paper also presents some ideas for monitoring the integrity of map-matching algorithms. The map-matching algorithms considered in this paper are generic and do not assume knowledge of ‘future’ information (i.e. based on either cost or time). Clearly, such data would result in relatively simple map-matching algorithms.     

Vehicle reidentification and travel time measurement across freeway junctions using the existing detector infrastructure

Conventional vehicle detectors are capable of monitoring discrete points along the freeway but do not provide information about conditions on the link between detectors. Knowledge of conditions on the link is useful to operating agencies for enabling timely decisions in response to various delay causing events and hence to reduce the resulting congestion of the freeway system. This paper presents an approach that matches vehicle measurements between detector stations to provide information on the conditions over the link between the detectors rather than relying strictly on the aggregate point measurements from the detectors. In particular this work reidentifies measurements from distinct vehicles using the existing loop detector infrastructure. Here the distinct vehicles are the long vehicles, but depending on the vehicle population or type of detector used, one might chose to use some other reproducible feature.This new methodology represents an important advancement over preceding loop based vehicle reidentification, as illustrated herein, it enables vehicle reidentification across a major diverge and a major merge. The examples include a case where the reidentification algorithm responded to delay between two detector stations an hour before the delay was locally observable at either of the stations used for reidentification. While previous loop based reidentification work was limited to dual loop detectors, the present effort also extends the methodology to single loop detectors; thereby making it more widely applicable. Although the research uses loop detector data, the algorithm would be equally applicable to data obtained from many other traffic detectors that provide reproducible vehicle features.     

How driver behaviour and parking alignment affects inductive charging systems for electric vehicles

Inductive charging, a form of wireless charging, uses an electromagnetic field to transfer energy between two objects. This emerging technology offers an alternative solution to users having to physically plug in their electric vehicle (EV) to charge. Whilst manufacturers claim inductive charging technology is market ready, the efficiency of transfer of electrical energy is highly reliant on the accurate alignment of the coils involved. Therefore understanding the issue of parking misalignment and driver behaviour is an important human factors question, and the focus of this paper. Two studies were conducted, one a retrospective analysis of 100 pre-parked vehicles, the second a dynamic study where 10 participants parked an EV aiming to align with a charging pad with no bay markings as guidance. Results from both studies suggest that drivers are more accurate at parking laterally than in the longitudinal direction, with a mean lateral distance from the centre of the bay being 12.12 and 9.57 cm (retrospective and dynamic studies respectively) compared to longitudinally 23.73 and 73.48 cm. With current inductive charging systems having typical tolerances of approximately ±10 cm from their centre point, this study has shown that only 5% of vehicles in both studies would be aligned sufficiently accurately to allow efficient transfer of electrical energy through induction.     

China’s electric car frustrations

By 2020, the vehicle population in China will likely exceed 280 million—exacerbating national energy security, urban air pollution, and traffic congestion. In response, many local and regional governments in China are pursuing an expanding array of measures to restrain growth in personal vehicle ownership and, along with the central government, reducing emissions and energy use of vehicles. One prominent strategy is the promotion of new energy vehicles, especially plug-in electric vehicles (PEVs). Large subsidies were offered—up to $27,600 (171,000 RMB) per vehicle in some regions, including almost $9200 (57,000 RMB) from the central government—which suggests that China is making a major commitment to PEVs. But sales have been meager. In 2013, only 17,600 PEVs, mostly buses and utility trucks, were sold, less than 0.1% of total civilian vehicle sales. Several factors explain the failure of PEV sales to take off: (1) protectionism by local governments; (2) uncertainty over which electric-drive vehicle technologies to promote and what consumers are willing to pay, (3) lagging investments in charging infrastructure, and (4) conservative investment behavior by automakers and battery manufacturers. The central government issued directives to local governments in late 2013 to reduce barriers to out-of-town companies, resulting in modest sales increases in early 2014, but a more coherent, broader, and effective set of policies, incentives, and strategies are needed to overcome consumer and industry resistance and the lack of charging infrastructure.     

Fuel savings on a heavy vehicle via aerodynamic drag reduction

In this numerical study, the fuel-saving potentials of drag-reducing devices retrofitted on heavy vehicles are analysed. Realistic on-road operations are taken into account by simulating typical driving routes on long-haul and urban distributions; variations in vehicle weight are also considered. Results show that the performance of these aerodynamic devices depend both on their functions and how the vehicles are operated. Vehicles on long-haul routes generally save twice as much fuel as those driven in urban areas. The fuel reductions from using selected devices individually on a large truck range from less than 1% to almost 9% of the fuel cost of a vehicle doing an annual mileage is 80,000 miles.     

Delay-based passenger car equivalents for trucks at signalized intersections

This paper presents a new methodology for computing passenger car equivalents at signalized intersections that is based on the delay concept. Unlike the commonly used headway-based methods that consider only the excess headway consumed by trucks, the delay-based approach fully considers the additional delay heavy vehicles cause on traffic stream. Delay-based passenger car equivalents are not constant, but depend on traffic volume, truck type and truck percentage. The field data indicated that the passenger car equivalents increase as the traffic volume and the percentage of heavy vehicles increase. The field data were used to calibrate TRAF-NETSIM simulation model that was used to cover a broad range of traffic conditions. Mathematical models to estimate the equivalencies were developed. The passenger car equivalent for single unit trucks vary from 1.00 to 1.37, and for combination trucks 1.00–2.18 depending on traffic volume and truck percentage. The passenger car equivalents are highly correlated with traffic volume and, to some degree, with percentage of heavy vehicles. Although the PCE of 1.5 recommended in the 1985 HCM seems to be more reasonable than the 2.0 recommended in the 1994 and 1997 HCM, both overestimate the impact of single unit trucks. For combination trucks, the 1997 HCM overestimates the capacity reduction effects of the trucks in most cases.     

Improved vehicle classification from dual-loop detectors in congested traffic

Vehicle classification is an important traffic parameter for transportation planning and infrastructure management. Length-based vehicle classification from dual loop detectors is among the lowest cost technologies commonly used for collecting these data. Like many vehicle classification technologies, the dual loop approach works well in free flow traffic. Effective vehicle lengths are measured from the quotient of the detector dwell time and vehicle traversal time between the paired loops. This approach implicitly assumes that vehicle acceleration is negligible, but unfortunately at low speeds this assumption is invalid and length-based classification performance degrades in congestion.To addresses this problem, we seek a solution that relies strictly on the measured effective vehicle length and measured speed. We analytically evaluate the feasible range of true effective vehicle lengths that could underlie a given combination of measured effective vehicle length, measured speed, and unobserved acceleration at a dual loop detector. From this analysis we find that there are small uncertainty zones where the measured length class can differ from the true length class, depending on the unobserved acceleration. In other words, a given combination of measured speed and measured effective vehicle length falling in the uncertainty zones could arise from vehicles with different true length classes. Outside of the uncertainty zones, any error in the measured effective vehicle length due to acceleration will not lead to an error in the measured length class. Thus, by mapping these uncertainty zones, most vehicles can be accurately sorted to a single length class, while the few vehicles that fall within the uncertainty zones are assigned to two or more classes. We find that these uncertainty zones remain small down to about 10 mph and then grow exponentially as speeds drop further.Using empirical data from stop-and-go traffic at a well-tuned loop detector station the best conventional approach does surprisingly well; however, our new approach does even better, reducing the classification error rate due to acceleration by at least a factor of four relative to the best conventional method. Meanwhile, our approach still assigns over 98% of the vehicles to a single class.     

The environmental impact of distribution to retail channels: A case study on packaged beverages

This study aims to quantify the environmental impact of two retail distribution networks and offer consumers a channel that is more beneficial to the environment. The environmental impact of replenishing the packaged beverages sold in convenience stores (CVSs) and hypermarkets is assessed using the streamlined life-cycle assessment (LCA) approach. In this study, the life-cycle impact assessment (LCIA) for packaged beverages up to the point of sale illustrates the distribution-level environmental impacts of truck transportation. The matched-pair t-test shows that the environmental impacts of transporting 1800 cartons of 24-pack/10 oz. beverages to be sold in Carrefour and 7-11 stores is different at a significance level of 0.1. The transport-focused LCA is used to improve the understanding and compare the environmental characteristics of the two distribution and retail systems. This study constitutes a vehicle for communicating to both internal and external stakeholders the environmental profiles of distributing the same product sold through two retail channels.     

Integrated analysis of GHGs and public health damage mitigation for developing urban road transportation strategies

This study attempts to present an urban road transportation strategy focusing on the mitigation of both GHGs emission and public health damage, taking Xiamen City as a case study. We developed a Public Health and GHGs Emission model to estimate the impacts of direct energy-consumption-related GHGs emissions and public health damage in Xiamen’s road transportation strategies from 2008 to 2025, considering the environmental benefits and economic costs. Two scenarios were designed to describe future transportation strategies for Xiamen City, and mitigation potentials for both GHGs emission and public health costs were estimated from 2008 to 2025 under a series of options. The results show that enacting controls on private vehicles would be most effective to GHGs mitigation, while enacting controls on government and rental vehicles would contribute the most to NO₂ and PM2.5 reductions. Compared with the Business as Usual scenario, the Integrated scenario would achieve about a 68% energy consumption reduction and save 0.23 billion yuan (95% CI: 0.16, 0.32) in health costs in 2025. It is clear that integrated and advisable strategies need to mitigate the adverse impacts of urban road vehicles on GHGs emissions and public health and economic costs, particularly in regions of rapid urbanization.     

On the electrification of road transportation – A review of the environmental,economic, and social performance of electric two-wheelers

Electrification is widely considered as a viable strategy for reducing the oil dependency and environmental impacts of road transportation. In pursuit of this strategy, most attention has been paid to electric cars. However, substantial, yet untapped, potentials could be realized in urban areas through the large-scale introduction of electric two-wheelers. Here, we review the environmental, economic, and social performance of electric two-wheelers, demonstrating that these are generally more energy efficient and less polluting than conventionally-powered motor vehicles. Electric two-wheelers tend to decrease exposure to pollution as their environmental impacts largely result from vehicle production and electricity generation outside of urban areas. Our analysis suggests that the price of e-bikes has been decreasing at a learning rate of 8%. Despite price differentials of 5000 ± 1800 EUR₂₀₁₂ kW h⁻¹ in Europe, e-bikes are penetrating the market because they appear to offer an apparent additional use value relative to bicycles. Mid-size and large electric two-wheelers do not offer such an additional use value compared to their conventional counterparts and constitute niche products at price differentials of 700 ± 360 EUR₂₀₁₂ kW⁻¹ and 160 ± 90 EUR₂₀₁₂ kW⁻¹, respectively. The large-scale adoption of electric two-wheelers can reduce traffic noise and road congestion but may necessitate adaptations of urban infrastructure and safety regulations. A case-specific assessment as part of an integrated urban mobility planning that accounts, e.g., for the local electricity mix, infrastructure characteristics, and mode-shift behavior, should be conducted before drawing conclusions about the sustainability impacts of electric two-wheelers.     

Reconstructing maximum likelihood trajectory of probe vehicles between sparse updates

Data from connected probe vehicles can be critical in estimating road traffic conditions. Unfortunately, current available data is usually sparse due to the low reporting frequency and the low penetration rate of probe vehicles. To help fill the gaps in data, this paper presents an approach for estimating the maximum likelihood trajectory (MLT) of a probe vehicle in between two data updates on arterial roads. A public data feed from transit buses in the city of San Francisco is used as an example data source. Low frequency updates (at every 200 m or 90 s) leaves much to be inferred. We first estimate travel time statistics along the road and queue patterns at intersections from historical probe data. The path is divided into short segments, and an Expectation Maximization (EM) algorithm is proposed for allocating travel time statistics to each segment. Then the trajectory with the maximum likelihood is generated based on segment travel time statistics. The results are compared with high frequency ground truth data in multiple scenarios, which demonstrate the effectiveness of the proposed approach, in estimating both the trajectory while moving and the stop positions and durations at intersections.     

Speed estimation and length based vehicle classification from freeway single-loop detectors

Roadway usage, particularly by large vehicles, is one of the fundamental factors determining the lifespan of highway infrastructure. Operating agencies typically employ expensive classification stations to monitor large vehicle usage. Meanwhile, single-loop detectors are the most common vehicle detector and many new, out-of-pavement detectors seek to replace loop detectors by emulating the operation of single-loop detectors. In either case, collecting reliable length data from these detectors has been considered impossible due to the noisy speed estimates provided by conventional data aggregation at single-loop detectors. This research refines non-conventional techniques for estimating speed at single-loop detectors, yielding estimates that approach the accuracy of a dual-loop detector’s measurements. Employing these speed estimation advances, this research brings length based vehicle classification to single-loop detectors (and by extension, many of the emerging out-of-pavement detectors). The classification methodology is evaluated against concurrent measurements from video and dual-loop detectors. To capture higher truck volumes than empirically observed, a process of generating synthetic detector actuations is developed. By extending vehicle classification to single-loop detectors, this work leverages the existing investment deployed in single-loop detector count stations and real-time traffic management stations. The work also offers a viable treatment in the event that one of the loops in a dual-loop detector classification station fails and thus, also promises to improve the reliability of existing classification stations.