Short-term traffic prediction is an important component of traffic management systems. Around logistics hubs such as seaports, truck flows can have a major impact on the surrounding motorways. Hence, their prediction is important to help manage traffic operations. However, The link between short-term dynamics of logistics activities and the generation of truck traffic has not yet been properly explored. This paper aims to develop a model that predicts short-term changes in truck volumes, generated from major container terminals in maritime ports. We develop, test, and demonstrate the model for the port of Rotterdam. Our input data are derived from exchanges of operational logistics messages between terminal operators, carriers and shippers, via the local Port Community System. We propose a feed-forward neural network to predict the next one hour of outbound truck traffic. To extract hidden features from the input data and select a model with appropriate features, we employ an evolutionary algorithm in accordance with the neural network model. Our model predicts outbound truck volumes with high accuracy. We formulate 2 scenarios to evaluate the forecasting abilities of the model. The model predicts lag and non-proportional responses of truck flows to changes in container turnover at terminals. The findings are relevant for traffic management agencies to help improve the efficiency and reliability of transport networks, in particular around major freight hubs.@en

Congestion, a frequent problem on freeways, is often considered a major challenge for the operations of road freight transport. Trucks, the main choice for road freight, not only suffer from congestion but they also contribute to it. Consequently, billions of dollars are lost worldwide in trucking operations, which also impedes economic growth and prosperity. Understanding driving behavior and on-trip decision-making of truck drivers are critically important to design measures that mitigate the impacts of congestion on truck traffic, and vice versa, to design measures that mitigate the impacts of truck traffic on congestion. In this respect, the on-trip behavior of truck drivers can be decomposed—like driving behavior in general—into strategical, tactical, and operational behavior, depicting route choice, short-term path-planning (e.g. merging, lane changing), and the steering & accelerating of the vehicle, respectively. Whereas these on-trip behaviors have been studied in-depth for drivers of passenger cars, there are larger gaps in our knowledge when it comes to strategical, tactical and operational behavior of trucks. Furthermore, our limited insight into the driving behavior of truck drivers inhibits the design of appropriate traffic control and management measures. To improve freight and traffic operations on freeways, this dissertation focuses on obtaining insights into the on-trip behavior of truck drivers and influencing this behavior for congestion relief. To this end, this dissertation develops new mathematical models and control methods for the strategical, tactical and operational behavior of truck drivers by analyzing emerging datasets and designing novel cooperative intelligent transportation system (C-ITS) applications.....@en

Cooperative intelligent transportation systems (C-ITS) support the exchange of information between vehicles and infrastructure (V2I or I2V). This paper presents an in-vehicle C-ITS application to improve traffic efficiency around a merging section. The application balances the distribution of traffic over the available lanes of a freeway, by issuing targeted lane-changing advice to a selection of vehicles. We add to existing research by embedding multiple vehicle classes in the lane-changing advisory framework. We use a multi-class multi-lane macroscopic traffic flow model to design a feedback-feedforward control law that is based on a linear quadratic regulator (LQR). The weights of the LQR controller are fine-tuned using a response surface method. The performance of the proposed system is evaluated using a microscopic traffic simulator. The results indicate that the multi-class lane-changing advisory system is able to suppress shockwaves in traffic flow and can significantly alleviate congestion. Besides bringing substantial travel time benefits around merging sections of up to nearly 21%, the system dramatically reduces the variance of travel time losses in the system. The proposed system also seems to improve travel times for mainline and ramp vehicles by nearly 20% and 42%, respectively.@en

This paper studies and compares the gap selection process of multiple vehicle classes (passenger cars, delivery vans, and trucks) within their discretionary lane changing activities. Given a trajectory or a sequence of gap selection decisions, we aim to predict whether a vehicle will change or keep a lane. For this purpose, we use a large trajectory dataset, collected for the Netherlands, consisting of 3,647 trajectories of passenger car drivers, 1,080 trajectories of delivery van drivers, and 2,226 trajectories of truck drivers. We apply gated recurrent unit neural networks to separately model their gap selection processes. These three models can not only handle class imbalance but also capture long-term interdependencies. The models can predict gap selection of three vehicle classes with geometric mean accuracies of 84% or higher. To obtain insights into their gap selection processes, we apply a gradient-based technique to analyze what neural networks have learned. Our results suggest that there exist significant differences between vehicle classes in terms of the importance of historical information and features. Trucks seem to value a relatively long period, recent 6 seconds, of driving experience to select gaps compared to passenger cars and delivery vans. In addition, the perception of road topology seems to be a significant factor for delivery vans and trucks, contrary to passenger cars which highly value their kinematic features and interactions with surrounding vehicles, to select gaps. These insights offer a novel contribution towards better understanding and modeling of the driving behavior of multiple vehicle classes.@en

Cooperative intelligent transportation systems (C-ITS) support the exchange of information between vehicles and infrastructure (V2I or I2V). This paper presents an in-vehicle C-ITS application to improve traffic efficiency around a merging section. This application balances the distribution of traffic over the available lanes of a freeway, by issuing targeted lane-changing advice to a selection of vehicles. We add to existing research by embedding multiple vehicle classes in the lane-changing advisory framework. We use a multi-class multi-lane macroscopic traffic flow model to design a feedback-feedforward control law that is based on a linear quadratic regulator (LQR). The performance of the proposed system is evaluated using a microscopic traffic simulator. The results indicate that the lane-changing advisory system is able to suppress Shockwaves in traffic flow and can significantly alleviate congestion. Besides bringing substantial travel time benefits around merging sections of up to nearly 21%, the system dramatically reduces the variance of travel time losses in the system.@en

This paper proposes a data-driven transport modeling framework to assess the impact of freight departure time shift policies. We develop and apply the framework around the case of the port of Rotterdam. Container transport demand data and traffic data from the surrounding network are used as inputs. The model is based on a graph convolutional deep neural network that predicts traffic volume, speed, and vehicle loss hours in the system with high accuracy. The model allows us to quantify the benefits of different degrees of adjustment of truck departure times towards the off-peak hours. In our case, travel time reductions over the network are possible up to 10%. Freight demand management can build on the model to design departure time advisory schemes or incentive schemes for peak avoidance by freight traffic. These measures may improve the reliability of road freight operations as well as overall traffic conditions on the network.@en

Floating car data present a cost-effective approach to observing the traffic state. This paper explores whether floating cars can substitute stationary detection devices (e.g., induction loops) for observers within traffic responsive control systems. A rule-based traffic control method at the local intersection level is proposed in this paper by utilizing the floating car data. The control method involves a three-fold approach: link-level speed forecasting, data-driven traffic flow estimation, and split optimization. To estimate traffic flow, a multivariable linear regression model is developed by utilizing forecasted link-level speed, signal control variables, and link length as predictors. The method is tested using a controller (hardware)-independent software-in-the-loop approach. Compared with the existing fixed-time control operating in Starnberg, Germany, the proposed method is able to improve the level of service of the signalized intersection when tested for different levels of market penetration of the floating cars. The findings underpin the use of floating car data in online traffic control applications; the benefits will increase with an increase in market penetration of floating cars. Overall, this paper presents a fully integrated technical system that is ready to be used in the field. The proposed system can be implemented at the tactical level of urban traffic-control hierarchy employed in Germany.@en

Optimizing route choices for truck drivers is a key element in achieving reliable road freight operations. For commercial reasons, it is often difficult to collect freight activity data through traditional surveys. Automated vehicle identification (AVI) data on fixed locations (e.g., Bluetooth or camera) are low-cost alternatives that may have the potential to estimate route choice models. However, in cases where these AVI sensors are sparsely located, the resulting data lack actual route choices (or labels), which limits their application estimating route choice models. This paper overcomes this limitation with a new two-step approach based on fusing AVI and loop-detector data. First, a sparse Bluetooth data set is fused with travel times estimated from densely spaced loop-detector data. Second, the combined data set is fed into a bi-objective optimization method which simultaneously infers the actual route choices of truck drivers between an origin–destination pair and estimates the parameters of a route choice (discrete choice-based) model. We apply this approach to investigate the route choice behavior of truck drivers operating to and from the port of Rotterdam in the Netherlands. The proposed model can distinguish between peak and off-peak periods and identify different segments of truck drivers based on a latent classes choice analysis. Our results indicate the potential of traffic and logistics interventions in improving the route choices of truck drivers during peak hours. Overall, this paper demonstrates that it might be possible to estimate route choice characteristics from readily available data that can be retrieved from traffic management agencies.@en

On important truck-dominated motorways, a large share of traffic consists of trucks. Our hypothesis is that these trucks do not always make optimal routing decisions which cause inefficiencies in the traffic system. Therefore, route choice of truck drivers is of interest to both transport planners and traffic management authorities. The objectives of this paper are two-fold. First, this paper models on-Trip route choices of the truck drivers. Second, we assess the inefficiencies of those routing decisions. This paper utilizes Bluetooth data, loop detector data, and variable message sign data to model the route choices of truck drivers. To the best of our knowledge, this is the first time that Bluetooth data have been used for the estimation of route choice models of truck drivers. The trucks are inferred from Bluetooth data by applying a Gaussian mixture model-based clustering technique. We apply both a binary logit model and a mixed logit model to derive the route choices of truck drivers on a case study between the port of Rotterdam and hinterland in the Netherlands. The predictive performance of the model is tested by conducting out-of-sample validation. The model results indicate truck drivers significantly value travel distance, instantaneous travel time and lane closure information en-route. The estimate of travel distance varies significantly among truck drivers. While 38% of truck drivers do not take the shortest time path, 48% of truck drivers do not choose the system-optimal path. These inefficiencies imply that traffic management solutions have the potential to improve the performance of truck-dominated motorways.@en

Truck platooning is an application of cooperative adaptive cruise control (CACC) which relies on vehicle-to-vehicle communications facilitated by vehicle ad-hoc networks. Communication uncertainties can affect the performance of a CACC controller. Previous research has not considered the full spectrum of possible car-following scenarios needed to understand how the longitudinal behaviour of truck platoons would be affected by changes in the communication network. In this paper, we investigate the impact of radio channel parameters on the string stability and collision avoidance capabilities of a CACC controller governing the longitudinal behaviour of truck platoons in a majority of critical car-following situations. We develop and use a novel, sophisticated and open-source VANET simulator OTS-Artery, which brings microscopic traffic simulation, network simulation, and psychological concepts in a single environment, for our investigations. Our results indicate that string stability and safety of truck platoons are mostly affected in car-following situations where truck platoons accelerate from the standstill to the maximum speed and decelerate from the maximum speed down to the standstill. The findings suggest that string stability can be improved by increasing transmission power and lowering receiver sensitivity. However, the safety of truck platoons seems to be sensitive to the choice of the path loos model.@en

Truck platooning is the application of cooperative adaptive cruise control where multiple trucks are electronically linked using vehicle-to-vehicle communication. Although truck platoons might bring fuel savings and emission reductions, their interactions with surrounding traffic and resulting impact on traffic operations and safety are not fully understood. The objective of this paper is to evaluate traffic efficiency and safety in a critical traffic situation when truck platoons are introduced in the system. This paper presents a case study of a merging section, located on A15 motorway, near the port of Rotterdam in the Netherlands. We consider two scenarios: platoons on a mainline carriageway and platoons merging onto a mainline carriageway. We simulate the movements of truck platoons in a microscopic traffic simulator. Longitudinal and lateral controllers for truck platoons, proposed in this paper, can ensure their collision-free, string-stable, and smooth driving behavior. Simulation experiments are conducted by varying platoon characteristics such as market penetration, length, intra-platoon headway, platoon speed, and their ability to create a gap for changing lanes. The results suggest that truck platoons on the mainline carriageway may be detrimental to traffic efficiency and safety in high traffic intensity, whereas truck platoons originating from an on-ramp produce limited impacts. Further, we conduct both local and global sensitivity analyses to analyze the impact of platoon characteristics on traffic efficiency and safety. The findings emphasize that uncertainty in traffic efficiency and safety strongly depends on the interactions among platoon characteristics, traffic demand, and traffic scenarios.@en

Lane-changing models are essential components for microscopic simulation. Although the literature recognizes that different classes of vehicles have different ways of performing lane-change maneuvers, lane change behavior of truck drivers is an overlooked research area. We propose that truck drivers are heterogeneous in their lane change behavior too and that inter-driver differences within truck drivers exist. We explore lane changing behavior of truck drivers using a trajectory data set collected around motorway bottlenecks in the Netherlands which include on-ramp, off-ramp, and weaving sections. Finite mixture models are used to categorize truck drivers with respect to their merging and diverging maneuvers. Indicator variables include spatial, temporal, kinematic, and gap acceptance characteristics of lane-changing maneuvers. The results suggest that truck drivers can be categorized into two and three categories with respect to their merging and diverging behaviors, respectively. The majority of truck drivers show a tendency to merge or diverge at the earliest possible opportunity; this type of behavior leads to most of the lane change activity at the beginning of motorway bottlenecks, thus contributing to the raised level of turbulence. By incorporating heterogeneity within the lane-changing component, the accuracy and realism of existing microscopic simulation packages can be improved for traffic and safety-related assessments.@en

In gateway seaports, like the port of Rotterdam, a substantial proportion of all freight movements is related to trips to hinterland markets. Accordingly, outbound truck flows from port areas, especially in traffic rush-hours, may degrade the level of service on truck-dominated motorways or increase the unreliability of freight transport operation. Therefore, these truck flows during traffic rush hours are of particular interest to both port and road transport authorities.Consequently, the main objective of this paper is to identify key features of port activities that induce truck traffic during rush hours by using both terminal activity data, at the container level, and truck-specific counts obtained from loop detector data for the year 2015. In this paper, we focus on inbound/outbound truck traffic. From our analysis, we find that terminals operational attributes such as estimated pick up time and container discharge time contribute mostly to the rush hour truck traffic. Besides, we identify the vessel attributes (call size), container features (size and type), and commodities which brings inefficiencies in the traffic system. Our research would be of interest to traffic managers, port of authority, and freight forwarders to invest in interventions which could improve the reliability of road freight operations.@en