Long distance truck tracking from advanced point detectors using a selective weighted Bayesian model

Truck flow patterns are known to vary by season and time-of-day, and to have important implications for freight modeling, highway infrastructure design and operation, and energy and environmental impacts. However, such variations cannot be captured by current truck data sources such as surveys or point detectors. To facilitate development of detailed truck flow pattern data, this paper describes a new truck tracking algorithm that was developed to estimate path flows of trucks by adopting a linear data fusion method utilizing weigh-in-motion (WIM) and inductive loop point detectors. A Selective Weighted Bayesian Model (SWBM) was developed to match individual vehicles between two detector locations using truck physical attributes and inductive waveform signatures. Key feature variables were identified and weighted via Bayesian modeling to improve vehicle matching performance. Data for model development were collected from two WIM sites spanning 26 miles in California where only 11 percent of trucks observed at the downstream site traversed the whole corridor. The tracking model showed 81 percent of correct matching rate to the trucks declared as through trucks from the algorithm. This high accuracy showed that the tracking model is capable of not only correctly matching through vehicles but also successfully filtering out non-through vehicles on this relatively long distance corridor. In addition, the results showed that a Bayesian approach with full integration of two complementary detector data types could successfully track trucks over long distances by minimizing the impacts of measurement variations or errors from the detection systems employed in the tracking process. In a separate case study, the algorithm was implemented over an even longer 65-mile freeway section and demonstrated that the proposed algorithm is capable of providing valuable insights into truck travel patterns and industrial affiliation to yield a comprehensive truck activity data source.     

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.     

Integrated data-driven modeling to estimate PM2.5 pollution from heavy-duty truck transportation activity over metropolitan area

Based on the national emission inventory data from different countries, heavy-duty trucks are the highest on-road PM_2.5 emitters and their representation is estimated disproportionately using current modeling methods. This study expands current understanding of the impact of heavy-duty truck movement on the overall PM_2.5 pollution in urban areas through an integrated data-driven modeling methodology that could more closely represent the truck transportation activities. A detailed integrated modeling methodology is presented in the paper to estimate urban truck related PM_2.5 pollution by using a robust spatial regression-based truck activity model, the mobile source emission and Gaussian dispersion models. In this research, finely resolved spatial–temporal emissions were calculated using bottom-up approach, where hourly truck activity and detailed truck-class specific emissions rates are used as inputs. To validate the proposed methodology, the Cincinnati urban area was selected as a case study site and the proposed truck model was used with U.S. EPA’s MOVES and AERMOD models. The heavy-duty truck released PM_2.5 pollution is estimated using observed concentrations at the urban air quality monitoring stations. The monthly air quality trend estimated using our methodology matches very well with the observed trend at two different continuous monitoring stations with Spearman’s rank correlation coefficient of 0.885. Based on emission model results, it is found that 71 percent of the urban mobile-source PM_2.5 emissions are caused by trucks and also 21 percent of the urban overall ambient PM_2.5 concentrations can be attributed to trucks in Cincinnati urban area.     

The effect of the competition between cars and trucks on the evolution of the motorway transport system

Motorways, which were devised at the beginning of their history as dedicated roads intended to be traveled by cars only, are at present also traveled by considerable flows of trucks. This fact has deeply changed the motorway transport system with respect to its original conception, owing to the interactions between two categories of vehicles whose characteristics are very different. These interactions greatly increase the transport cost perceived by car drivers with respect to truck drivers. This paper studies the consequences of this cost asymmetry on the evolution of the transport system when the geometric characteristics of a motorway remain unchanged in time, while transport demand increases. By using a theoretical model of competition between cars and trucks, it is shown that, if both the geometric characteristics of a motorway and the increase rate of the activities that feed the transport demand remain unchanged over time, the competition between cars and trucks, as well as the fact that in general passengers have better transport alternatives than freight, make the increase rate of truck traffic greater than that of cars, causing a progressive increase in the proportion of trucks in the time periods in which a motorway is traveled by both the vehicle categories. Since truck traffic on motorways, at least in Europe, is very scarce on weekends and in holiday periods, in which motorways are traveled almost only by cars, these results seem to indicate a tendency to the specialization of motorways, which are likely to be used in the future mostly by only one category of vehicles in different periods of time.     

A model for national freight flows,distribution centers,empty trucks and urban truck movements

Trucks travel both short distances for local deliveries and long distances for transporting goods across the country. Often their travel behavior is tour-based, they run under tight schedules and under curfew on selected roads. Despite these differences from personal travel, in practice truck models largely follow person travel methods. To overcome this shortcoming, a two-layer truck model is developed for the Chicago Metropolitan Area. Long-distance trucks are driven by commodity flows, with distribution centers, rail yards, marine ports and airports being represented explicitly. Empty trucks are accounted for as well. For the short-distance truck model, a novel parameter estimation method makes use of limited data to derive region-specific parameters. The model is fully operational and validates reasonably well against traffic counts.     

Collaborative truck scheduling and appointments for trucking companies and container terminals

Appointment systems for truck arrivals at container terminals have been applied in many ports to reduce truck congestion. This study suggests a new appointment process by which trucking companies and terminals collaboratively determine truck operation schedules and truck arrival appointments. This study formulates a mathematical model involving a sub-problem for each trucking company to determine the optimal dispatching schedules for trucks and the other sub-problem for the terminal to estimate the expected truck system time in each time interval. An iterative collaboration process is proposed based on a decomposed mathematical formulation. Numerical experiments are conducted to investigate the performance of the decision process and the robustness of the process in practical operation conditions.     

Estimating the environmental costs of port related emissions: The case of Kaohsiung

This study estimates the emission costs of ships and trucks in the Port of Kaohsiung, Taiwan, focusing mainly on particular matter and volatile organic compounds. By calculating annual ship and truck emissions we find that the major contributors are tankers, container ships and bulk ships and trucks. Using a bottom-up methodology, the combined environmental costs of ships and trucks are estimated to be over $123 million per year.     

Analysis of heavy truck accident severity

This paper presents a macro empirical study of injuries and severity, and their contributory factors, for heavy truck accidents. This analysis is carried out through the examination of the fatal injury and non-fatal injury odds ratio indices for a statistically best-fit, log-linear model. The statistical procedures used were the stepwise, backward elimination, log-linear modeling with a critical significance level of 0.05. The analysis was conducted on the set of accident severity contributory independent variables (main-effects) and all possible two-way interactions of these variables (interaction effects) for articulated heavy trucks. Among the findings of the study is that it would appear to be more effective to decrease the accident rates for these trucks rather than to try to limit their resulting severities in attempting to reduce the toll of heavy truck accidents.     

A derived demand model of regional highway diesel fuel use

A regional, econometric model of heavy truck diesel fuel use is derived based on the theory of production. Input demand functions for new trucks and diesel fuel are specified and estimated. A simple, logistic scrappage model is estimated and used to estimate total heavy truck stocks and diesel-engine heavy truck stocks. Demand equations based on the AIDS almost ideal demand system flexible form cost function are estimated for new heavy truck demand and regional highway diesel fuel demand. New heavy truck demand is found to be elastic with respect to GNP, inelastic with respect to own price, and appears extremely sensitive to short term GNP trends. The short run price elasticity of diesel fuel demand is found to be very small.     

Comparison of energy demands of drone-based and ground-based parcel delivery services

Drones are one of the most intensively studied technologies in logistics in recent years. They combine technological features matching current trends in transport industry and society like autonomy, flexibility, and agility. Among the various concepts for using drones in logistics, parcel delivery is one of the most popular application scenarios. Companies like Amazon test drones particularly for last-mile delivery intending to achieve both reducing total cost and increasing customer satisfaction by fast deliveries. As drones are electric vehicles, they are also often claimed to be an eco-friendly mean of transportation.In this paper an energy consumption model for drones is proposed to describe the energy demand for drone deliveries depending on environmental conditions and the flight pattern. The model is used to simulate the energy demand of a stationary parcel delivery system which serves a set customers from a depot. The energy consumed by drones is compared to the energy demand of Diesel trucks and electric trucks serving the same customers from the same depot.The results indicate that switching to a solely drone-based parcel delivery system is not worthwhile from an energetic perspective in most scenarios. A stationary drone-based parcel delivery system requires more energy than a truck-based parcel delivery system particularly in urban areas where customer density is high and truck tours are comparatively short. In rather rural settings with long distances between customers, a drone-based parcel delivery system creates an energy demand comparable to a parcel delivery system with electric trucks provided environmental conditions are moderate.     

Exploring the effects of cooperative adaptive cruise control on highway traffic flow using microscopic traffic simulation

This paper examines the impact of having cooperative adaptive cruise control (CACC) embedded vehicles on traffic flow characteristics of a multilane highway system. The study identifies how CACC vehicles affect the dynamics of traffic flow on a complex network and reduce traffic congestion resulting from the acceleration/deceleration of the operating vehicles. An agent-based microscopic traffic simulation model (Flexible Agent-based Simulator of Traffic) is designed specifically to examine the impact of these intelligent vehicles on traffic flow. The flow rate of cars, the travel time spent, and other metrics indicating the evolution of traffic congestion throughout the lifecycle of the model are analyzed. Different CACC penetration levels are studied. The results indicate a better traffic flow performance and higher capacity in the case of CACC penetration compared to the scenario without CACC-embedded vehicles.     

Preferences for autonomous and alternative fuel-powered heavy-duty trucks in Germany

Germany is by far the largest contributor of greenhouse gas emissions in the European Union but adopted its own climate action plan to achieve greenhouse gas neutrality by 2050. The country’s third-largest emitter of greenhouse gas emissions is the transportation sector. As of January 2019, 99.7% of heavy-duty trucks registered in Germany run on diesel while the share of alternative fuel-powered passenger cars increases steadily. Apart from rising emissions, the industry faces a growing shortage of qualified truck drivers. A solution to increasing emissions and the shortage of drivers are autonomous and alternative fuel-powered heavy-duty trucks. We employed a choice-based conjoint analysis with employees from freight companies in Germany to find out how they assess the main attributes of innovative trucks. Our results reveal that the maximum driving range is the most important attribute followed by the refueling/recharging time. Tank-to-wheel emissions, on the other hand, was ranked as the least relevant attribute. Moreover, we present customers’ preference shares for future heavy-duty trucks until 2035. According to our results, freight companies are generally open to switching from conventional to low emission and (conditionally-) automated heavy-duty trucks, however, a close collaboration between truck manufacturers, customers, infrastructure companies, and policymakers is essential to spur the penetration of autonomous and alternative fuel-powered heavy-duty trucks.     

货车自动驾驶技术及标准法规发展分析

目前,自动驾驶技术在乘用车领域已获得突破性发展;为提高通行效率和出行安全起到了极大的作用。自动驾驶技术在商用车领域的应用,有望较好解决高昂的人力成本和难以提高的效率等问题。然而,目前自动驾驶技术在货车的应用大多采用跟乘用车同样的标准进行规范,这在实际应用中存在着诸多的问题;例如,在FCW和AEB功能中货车在相同车速的制动距离要远大于乘用车,而其所采用标准规定的碰撞预警时间却并无多大不同,这在实际场景中存在着较大的安全隐患[1,2]。此外,货车质量和体积较大,且较多应用于长途运输,运输过程中会经历包括高温、严寒、山区等多种复杂气候场景,这些都将对货车自动驾驶技术在车辆制动效能、能耗以及多场景应用等方面提出更有针对性规范的要求。本文针对货车的应用场景特点,为其自动驾驶技术应用标准化提出了建议。     

Decisions on truck parking place and time on expressways: an analysis using digital tachograph data

With the objective of deriving useful insights into measures against traffic congestion at service areas (SAs) and parking areas (PAs) on expressways and ensuring efficient use of SAs/PAs, this study investigated the decisions on where a truck is parked (i.e., choice of an SA or a PA), how long it is parked (i.e., parking time), and their influential factors. To this end, this study used the trajectory data of 1600 trucks recorded in 6-min intervals by in-vehicle digital tachographs on the Sanyo and Chugoku Expressways in Japan from October 2013 to March 2014. First, the aspect of repeated choice of each truck (i.e., habitual behavior) toward a specific SA/PA was clarified. Next, a multilevel discrete–continuous model (Type II Tobit model) was developed to reveal the factors affecting the above decisions. The modeling results confirmed the existence of habitual behavior and showed that trucks were more likely to be parked a longer time at an SA/PA when it is closer to the destination. It appears that truck drivers may adjust their time at the SA/PA close to the destination to comply with the arrival time, which is often predetermined by the owner of the transported goods. Furthermore, the availability of restaurants and shops, and the number of parking spaces available for trucks and trailers are important determinants of parking time, whereas the existence of a convenience store is important to the choice of the SA/PA. Parking experience has an extremely strong positive effect on the parking choice and use. Moreover, increasing the number of parking lots may induce its longer use.

     

Unravelling effects of cooperative adaptive cruise control deactivation on traffic flow characteristics at merging bottlenecks

Cooperative Adaptive Cruise Control (CACC) systems have the potential to increase roadway capacity and mitigate traffic congestion thanks to the short following distance enabled by inter-vehicle communication. However, due to limitations in acceleration and deceleration capabilities of CACC systems, deactivation and switch to ACC or human-driven mode will take place when conditions are outside the operational design domain. Given the lack of elaborate models on this interaction, existing CACC traffic flow models have not yet been able to reproduce realistic CACC vehicle behaviour and pay little attention to the influence of system deactivation on traffic flow at bottlenecks. This study aims to gain insights into the influence of CACC on highway operations at merging bottlenecks by using a realistic CACC model that captures driver-system interactions and string length limits. We conduct systematic traffic simulations for various CACC market penetration rates (MPR) to derive free-flow capacity and queue discharge rate of the merging section and compare these to the capacity of a homogeneous pipeline section. The results show that an increased CACC MPR can indeed increase the roadway capacity. However, the resulting capacity in the merging bottleneck is much lower than the pipeline capacity and capacity drop persists in bottleneck scenarios at all CACC MPR levels. It is also found that CACC increases flow heterogeneity due to the switch among different operation modes. A microscopic investigation of the CACC operational mode and trajectories reveals a close relation between CACC deactivation, traffic congestion and flow heterogeneity.     

Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data

Vehicle longitudinal control systems such as (commercially available) autonomous Adaptive Cruise Control (ACC) and its more sophisticated variant Cooperative ACC (CACC) could potentially have significant impacts on traffic flow. Accurate models of the dynamic responses of both of these systems are needed to produce realistic predictions of their effects on highway capacity and traffic flow dynamics. This paper describes the development of models of both ACC and CACC control systems that are based on real experimental data. To this end, four production vehicles were equipped with a commercial ACC system and a newly developed CACC controller. The Intelligent Driver Model (IDM) that has been widely used for ACC car-following modeling was also implemented on the production vehicles. These controllers were tested in different traffic situations in order to measure the actual responses of the vehicles. Test results indicate that: (1) the IDM controller when implemented in our experimental test vehicles does not perceptibly follow the speed changes of the preceding vehicle; (2) strings of consecutive ACC vehicles are unstable, amplifying the speed variations of preceding vehicles; and (3) strings of consecutive CACC vehicles overcome these limitations, providing smooth and stable car following responses. Simple but accurate models of the ACC and CACC vehicle following dynamics were derived from the actual measured responses of the vehicles and applied to simulations of some simple multi-vehicle car following scenarios.     

Optimal tolls for multi-class traffic: Analytical formulations and policy implications

This paper puts together an analytical formulation to compute optimal tolls for multi-class traffic. The formulation is comprised of two major modules. The first one is an optimization component aimed at computing optimal tolls assuming a Stackelberg game in which the toll agency sets the tolls, and the equilibrating traffic plays the role of the followers. The optimization component is supported by a set of cost models that estimate the externalities as a function of a multivariate vector of traffic flows. These models were estimated using Taylor series expansions of the output obtained from traffic simulations of a hypothetical test case. Of importance to the paper is the total travel time function estimated using this approach that expresses total travel time as a multivariate function of the traffic volumes. The formulation presented in the paper is then applied to a variety of scenarios to gain insight into the optimality of current toll policies. The optimal tolls are computed for two different cases: independent tolls, and tolls proportional to passenger car equivalencies (PCE).The numerical results clearly show that setting tolls proportional to PCEs leads to lower values of welfare that are on average 15% lower than when using independent tolls, though, in some cases the total welfare could be up to 33% lower. This is a consequence of two factors. First, the case of independent tolls has more degrees of freedom than the case of tolls proportional to PCEs. Second, tolls proportional to PCEs do not account for externalities other than congestion, which is likely to lead to lower welfare values.The analytical formulations and numerical results indicate that, because the total travel time is a non-linear function of the traffic volumes, the marginal social costs and thus the optimal congestion tolls also depend on the traffic volumes for each vehicle class. As a result of this, for the relatively low volumes of truck traffic observed in real life, the optimal congestion tolls for trucks could indeed be either lower or about the same as for passenger cars. This stand in sharp contrast with what is implied in the use of PCEs, i.e., that the contribution to congestion are constant. This latter assumption leads to optimal truck congestion tolls that are always proportional to the PCE values.The comparison of the toll ratios (truck tolls divided by passenger car tolls) for both observed and optimal conditions suggests that the tolls for small trucks are about the right level, maybe a slightly lower than optimal. However, the analysis of the toll ratio for large trucks seems to indicate a significant overcharge. The estimates show that the average observed toll ratio for large trucks is even higher than the maximum optimal toll ratio found in the numerical experiments. This suggests that the tolls for large trucks are set on the basis of revenue generation principles while the passenger car tolls are being set based on a mild form of welfare maximization. This leads to a suboptimal cross-subsidization of passenger car traffic in detriment of an important sector of the economy.     

Dynamic traffic assignment of cooperative adaptive cruise control

Advances in connected and automated vehicle technologies have resulted in new vehicle applications, such as cooperative adaptive cruise control (CACC). Microsimulation models have shown significant increases in capacity and stability due to CACC, but most previous work has relied on microsimulation. To study the effects of CACC on larger networks and with user equilibrium route choice, we incorporate CACC into the link transmission model (LTM) for dynamic network loading. First, we derive the flow-density relationship from the MIXIC car-following model of CACC (at 100% CACC market penetration). The flow-density relationship has an unusual shape; part of the congested regime has an infinite congested wave speed. However, we verify that the flow predictions match observations from MIXIC modeled in VISSIM. Then, we use the flow-density relationship from MIXIC in LTM. Although the independence of separate links restricts the maximum congested wave speed, for common freeway link lengths the congested wave speed is sufficiently high to fit the observed flows from MIXIC. Results on a freeway and regional networks (with CACC-exclusive lanes) indicate that CACC could reduce freeway congestion, but naïve deployment of CACC-exclusive lanes could cause an increase in total system travel time.     

Dynamic charging infrastructure deployment for plug-in hybrid electric trucks

Inspired by the rapid development of charging-while-driving (CWD) technology, plans are ongoing in government agencies worldwide for the development of electrified road freight transportation systems through the deployment of dynamic charging lanes. This en route method for the charging of plug-in hybrid electric trucks is expected to supplement the more conventional charging technique, thus enabling significant reduction in fossil fuel consumption and pollutant emission from road freight transportation. In this study, we investigated the optimal deployment of dynamic charging lanes for plug-in hybrid electric trucks. First, we developed a multi-class multi-criteria user equilibrium model of the route choice behaviors of truck and passenger car drivers and the resultant equilibrium flow distributions. Considering that the developed user equilibrium model may have non-unique flow distributions, a robust deployment of dynamic charging lanes that optimizes the system performance under the worst-case flow distributions was targeted. The problem was formulated as a generalized semi-infinite min-max program, and a heuristic algorithm for solving it was proposed. This paper includes numerical examples that were used to demonstrate the application of the developed models and solution algorithms.