Passing Rates to Measure Relaxation and Impact of Lane‐Changing in Congestion

Abstract: Passing rate measurements of backward‐moving kinematic waves in congestion are applied to quantify two traffic features; a relaxation phenomenon of vehicle lane‐changing and impact of lane‐changing in traffic streams after the relaxation process is complete. The relaxation phenomenon occurs when either a lane‐changer or its immediate follower accepts a short spacing upon insertion and gradually resumes a larger spacing. A simple existing model describes this process with few observable parameters. In this study, the existing model is reformulated to estimate its parameter using passing rate measurements. Calibration results based on vehicle trajectories from two freeway locations indicate that the revised relaxation model matches the observation well. The results also indicate that the relaxation occurs in about 15 seconds and that the shoulder lane exhibits a longer relaxation duration. The passing rate measurements were also employed to quantify the postrelaxation impact of multiple lane‐changing maneuvers within a platoon of 10 or more vehicles in queued traffic stream. The analysis of the same data sets shows that lane‐changing activities do not induce a long‐term change in traffic states; traffic streams are perturbed temporarily by lane‐changing maneuvers but return to the initial states after relaxations.     

Methodology for spatio‐temporal predictions of traffic counts across an urban road network and generation of an on‐road greenhouse gas emission inventory

On‐road emission inventories in urban areas have typically been developed using traffic data derived from travel demand models. These approaches tend to underestimate emissions because they often only incorporate data on household travel, not including commercial vehicle movements, taxis, ride hailing services, and other trips typically underreported within travel surveys. In contrast, traffic counts embed all types of on‐road vehicles; however, they are only conducted at selected locations in an urban area. Traffic counts are typically spatially correlated, which enables the development of methods that can interpolate traffic data at selected monitoring stations across an urban road network and in turn develop emission estimates. This paper presents a new and universal methodology designed to use traffic count data for the prediction of periodic and annual volumes as well as greenhouse gas emissions at the level of each individual roadway and for multiple years across a large road network. The methodology relies on the data collected and the spatio‐temporal relationships between traffic counts at various stations; it recognizes patterns in the data and identifies locations with similar trends. Traffic volumes and emissions prediction can be made even on roads where no count data exist. Data from the City of Toronto traffic count program were used to validate the output of various algorithms, indicating robust model performance, even in areas with limited data.     

The use of micro-simulation for congested traffic load modeling of medium- and long-span bridges

This paper presents a new approach to the modeling of congested traffic loading events on long span bridges. Conventional traffic load models are based on weigh-in-motion data of non-congested traffic, or something similar to a Poisson Arrival process. In neither case do they account for the mixing between lanes that takes place as traffic becomes congested. It is shown here that cars move out from between trucks as traffic slows down which results in a higher frequency of long platoons of trucks in the slow lane of the bridge. These longer platoons increase some characteristic load effects under the slow lane by a modest but significant amount. Micro-simulation, the process of modeling individual vehicles that is widely used in traffic modeling, is presented here as a means of predicting imposed traffic loading on long-span bridges more accurately. The traffic flow on a congested bridge is modelled using a random mixing process for trucks and cars in each lane, where each vehicle is modelled individually with driver behaviour parameters assigned randomly in a Monte Carlo process. Over a number of simulated kilometres, the vehicles move between lanes in simulated lane-changing manoeuvres. The algorithm was calibrated against video recordings of traffic on a bridge in the Netherlands. Extreme value statistics of measured strains on the bridge are then compared to the corresponding simulation statistics to validate the model. The micro-simulation algorithm shows that the histograms of truck platoon length are moderately affected by lane changing. This in turn is shown to influence some characteristic load effects of the bridge deck.     

Robust Optimal Lane Allocation for Isolated Intersections

Lane allocation including approach and exit lane numbers and lane markings of approach lanes plays an important role in improving the capacity of an intersection. Conventional approaches for optimizing lane allocation often ignore fluctuations in traffic demand (TD). This article presents a stochastic model for robust optimal lane allocation of an isolated intersection under stochastic traffic conditions. This model is built in three steps. In the first step, an enhanced lane‐based model in the form of a binary mixed‐integer nonlinear program is proposed to optimize lane allocation and traffic signals for both vehicles and pedestrians in a unified framework under deterministic traffic conditions. In the second step, a two‐level stochastic model is developed to obtain a robust lane allocation that is less sensitive to traffic flow fluctuations considering the flexibility of traffic signals. In the third step, the two‐level model is further transformed into a TD‐based stochastic model in a two‐phase form to reduce the solution dimension for efficient computation. A TD‐based genetic algorithm procedure is presented for solvability. Numerical studies are conducted to validate the model formulations and solution algorithms.     

Long‐Distance‐Commuter (LDC) Lane: A New Concept for Freeway Traffic Management

Many freeways connect two major cities (e.g., Gold Coast to Brisbane) in Australia. A great portion of commuters travel between these two cities whereas the number of commuters taking the on‐ramps and off‐ramps in between is comparatively small. Enlightened by this observation, we propose a novel and practical concept for freeway traffic management: long‐distance‐commuter (LDC) lane. The LDC lane is a dedicated lane that only commuters traveling between the two major cities can use. Since there is no or only minor disturbance from forced lane‐changing of vehicles from other lanes, the LDC lane admits a much higher capacity. Our preliminary case study based on the freeway between Gold Coast and Brisbane demonstrates that using the LDC lane significantly increases the average speed as well as overall capacity.     

Empirical Innovation of Computational Dual‐Loop Models for Identifying Vehicle Classifications against Varied Traffic Conditions

Clarifying traffic flow phases is a primary requisite for applying length‐based vehicle classifications with dual‐loop data under various traffic conditions. One challenge lies in identifying traffic phases using variables that could be directly calculated from the dual‐loop data. This article presents an innovative approach and associated algorithm for identifying traffic phases through a hybrid method that incorporates level of service method and K‐means clustering method. The “phase representative variables” are identified to represent traffic characteristics in the traffic flow phase identification algorithm. The traffic factors influencing the vehicle classification accuracy under non‐free traffic conditions are successfully identified using video‐based vehicular trajectory data, and the innovative length‐based vehicle classification models are then developed. The result of the concept‐of‐evidence test with use of sample data indicates that compared with the existing model, the accuracy of the estimated vehicle lengths is increased from 42% to 92% under synchronized and stop‐and‐go conditions. The results also foster a better understanding of the traffic stream characteristics and associated theories to lay out a good foundation for further development of relevant microscopic simulation models with other sensing traffic data sources.     

Modeling Vehicle Interactions during Lane‐Changing Behavior on Arterial Streets

Abstract: Lane‐changing algorithms have attracted increased attention during recent years in traffic modeling. However, little has been done to address the competition and cooperation of vehicles when changing lanes on urban streets. The main goal of this study is to quantify the vehicle interactions during a lane‐changing maneuver. Video data collected at a busy arterial street in Gainesville, Florida, were used to distinguish between free, forced, and competitive/cooperative lane changes. Models particularly for competitive/cooperative lane changes were developed, depending on whether the following vehicle cooperates with the subject vehicle or not. By referring to the “TCP/IP” protocol in computer network communications, a sequence of “hand‐shaking” negotiations were designed to handle the competition and cooperation among vehicles. The developed model was implemented and validated in the CORSIM microsimulator package, with the simulation capabilities compared against the original lane‐changing model in CORSIM. The results indicate that the new model better replicates the observed traffic under different levels of congestion.     

Die zweite Geo‐Geum‐Brücke in Südkorea – Schrägkabelbrücke und Vorlandbrücke für Straßenverkehr mit Verbundfachwerkträger

The 2nd Geo Geum Grand Bridge in Korea – cable‐stayed bridge and approach bridge with composite truss girder for roadway traffic. The 2nd Geo Geum Grand Bridge forms part of the fixed connection from the mainland to the Island of Geo Geum in South Korea (Fig. 1). This high level crossing is composed of a 912 m long approach viaduct and a 1116 m long stay cable bridge with a main span of 480 m. The approach viaduct is a continuous girder with regular spans of 120 m. The superstructure of both bridges is a composite truss girder bridge, which carries a 2 lane highway on top and a pedestrian and bicycle way on the lower deck. The main design considerations were function as a high‐way, innovation in design and visual harmony with its surround‐ings. The stay cables are arranged in a single plane at the centre of the bridge, their semi‐fan arrangement with bundled configur‐ation gives a very special and unique appearance.     

Random Process Model for Urban Traffic Flow Using a Wavelet‐Bayesian Hierarchical Technique

Abstract: The existing well‐known short‐term traffic forecasting algorithms require large traffic flow data sets, including information on current traffic scenarios to predict the future traffic conditions. This article proposes a random process traffic volume model that enables estimation and prediction of traffic volume at sites where such large and continuous data sets of traffic condition related information are unavailable. The proposed model is based on a combination of wavelet analysis (WA) and Bayesian hierarchical methodology (BHM). The average daily “trend” of urban traffic flow observations can be reliably modeled using discrete WA. The remaining fluctuating parts of the traffic volume observations are modeled using BHM. This BHM modeling considers that the variance of the urban traffic flow observations from an intersection vary with the time‐of‐the‐day. A case study has been performed at two busy junctions at the city‐centre of Dublin to validate the effectiveness of the strategy.     

Two‐Dimensional Simulation of Turning Behavior in Potential Conflict Area of Mixed‐Flow Intersections

The potential conflict area of intersection is the space where conflicting traffic flows pass through in the same signal phase. At this area, turning vehicles interact with most traffic flows, which introduce complex features including variation of trajectories and shared‐priority phenomenon. The traditional one‐dimensional simulation oversimplifies these features with lane‐based assumption. This study integrates the modified social force model with behavior decision and movement constraints to reproduce the two‐dimensional turning process. The method is framed into a three‐layered mathematical model. First, the decision layer dynamically makes decision for turning patterns. Then the operation layer uses the modified social force model to initially generate vehicle movements. Finally, the constraint layer modifies the vehicular motion with vehicle dynamics constraints, boundary of intersection and the collision avoidance rule. The proposed model is validated using trajectories of left‐turn vehicles at a real‐world mixed‐flow intersection with nonprotected signal phases, resulting in a more realistic simulation than previous methods. The distributions of decision points and travel time in simulation are compared with the empirical data in statistics. Moreover, the spatial distribution of simulated trajectories is also satisfactory.     

Time‐Varying Lane‐Based Capacity Reversibility for Traffic Management

Abstract: This article presents a new bi‐level formulation for time‐varying lane‐based capacity reversibility problem for traffic management. The problem is formulated as a bi‐level program where the lower level is the cell‐transmission‐based user‐optimal dynamic traffic assignment (UODTA). Due to its Non‐deterministic Polynomial‐time hard (NP‐hard) complexity, the genetic algorithm (GA) with the simulation‐based UODTA is adopted to solve multiorigin multidestination problems. Four GA variations are proposed. GA1 is a simple GA. GA2, GA3, and GA4 with a jam‐density factor parameter (JDF) employ time‐dependent congestion measures in their decoding procedures. The four algorithms are empirically tested on a grid network and compared based on solution quality, convergence speed, and central processing unit (CPU) time. GA3 with JDF of 0.6 appears best on the three criteria. On the Sioux Falls network, GA3 with JDF of 0.7 performs best. The GA with the appropriate inclusion of problem‐specific knowledge and parameter calibration indeed provides excellent results when compared with the simple GA.     

Real‐time crack assessment using deep neural networks with wall‐climbing unmanned aerial system

Crack information provides important evidence of structural degradation and safety in civil structures. Existing inspection methods are inefficient and difficult to rapidly deploy. A real‐time crack inspection method is proposed in this study to address this difficulty. Within this method, a wall‐climbing unmanned aerial system (UAS) is developed to acquire detailed crack images without distortion, then a wireless data transmission method is applied to fulfill real‐time detection requirements, allowing smartphones to receive real‐time video taken from the UAS. Next, an image data set including 1,330 crack images taken by the wall‐climbing UAS is established and used for training a deep‐learning model. For increasing detection speed, state‐of‐the‐art convolutional neural networks (CNNs) are compared and employed to train the crack detector; the selected model is transplanted into an android application so that the detection of cracks can be undertaken on a smartphone in real time. Following this, images with cracks are separated and crack width is calculated using an image processing method. The proposed method is then applied to a building where crack information is acquired and calculated accurately with high efficiency, thus verifying the practicability of the proposed method and system.     

End‐to‐End Deep Learning Methodology for Real‐Time Traffic Network Management

This article presents a novel real‐time traffic network management system using an end‐to‐end deep learning (E2EDL) methodology. A computational learning model is trained, which allows the system to identify the time‐varying traffic congestion pattern in the network, and recommend integrated traffic management schemes to reduce this congestion. The proposed model structure captures the temporal and spatial congestion pattern correlations exhibited in the network, and associates these patterns with efficient traffic management schemes. The E2EDL traffic management system is trained using a laboratory‐generated data set consisting of pairings of prevailing traffic network conditions and efficient traffic management schemes designed to cope with these conditions. The system is applied for the US‐75 corridor in Dallas, Texas. Several experiments are conducted to examine the system performance under different traffic operational conditions. The results show that the E2EDL system achieves travel time savings comparable to those recorded for an optimization‐based traffic management system.     

Evaluation of Information Applications of a Self‐Organizing Distributed Traffic Information System for a Large‐Scale Real‐World Traffic Network

Abstract: This article presents an evaluation of the system performance of a proposed self‐organizing, distributed traffic information system based on vehicle‐to‐vehicle information‐sharing architecture. Using microsimulation, several information applications derived from this system are analyzed relative to the effectiveness and efficiency of the system to estimate traffic conditions along each individual path in the network, to identify possible incidents in the traffic network, and to provide rerouting strategies for vehicles to escape congested spots in the network. A subset of vehicles in the traffic network is equipped with specific intervehicle communication devices capable of autonomous traffic surveillance, peer‐to‐peer information sharing, and self‐data processing. A self‐organizing traffic information overlay on the existing vehicular roadway network assists their independent evaluation of route information, detection of traffic incidents, and dynamic rerouting in the network based both on historical information stored in an in‐vehicle database and on real‐time information disseminated through intervehicle communications. A path‐based microsimulation model is developed for these information applications and the proposed distributed traffic information system is tested in a large‐scale real‐world network. Based on simulation study results, potential benefits both for travelers with such equipment as well as for the traffic system as a whole are demonstrated.     

Die Wupper‐Talbrücke Oehde – eine moderne Verbundbrücke

The Wupper River valley bridge – a state‐of‐the‐art composite bridge. The German motorway Autobahn A 1 is one of the nation's most important and frequently traveled highways, connecting the country's northern and southern regions. The road currently has two lanes in each direction, which do not provide enough capacity to carry the traffic volume without daily traffic jams and heavy delays. Therefore, the federal government and the Northrhine‐Westfalia Department of Transportation decided to ease the situation by adding one lane in each direction. This total of 6 lanes fits into the master plan of widening major highways around the Ruhr River region. North of Cologne, the Autobahn A 1 crosses mountainous terrain, so several bridges with total lengths between 240 and 420 meters have had to be widened, replaced, or, most commonly, supplemented by a new bridge. Such supplementation was used on the Wupper River Valley Bridge, called Oehde, close to the city of Wuppertal. The bridge illustrates an example of modern composite bridges adopting new methods in construction and design, and also marks an outstanding example of current composite structures in Germany.     

Effect of Pulse‐and‐Glide Strategy on Traffic Flow for a Platoon of Mixed Automated and Manually Driven Vehicles

The fuel consumption of ground vehicles is significantly affected by how they are driven. The fuel‐optimized vehicular automation technique can improve fuel economy for the host vehicle, but their effectiveness on a platoon of vehicles is still unknown. This article studies the performance of a well‐known fuel‐optimized vehicle automation strategy, i.e., Pulse‐and‐Glide (PnG) operation, on traffic smoothness and fuel economy in a mixed traffic flow. The mixed traffic flow is assumed to be a single‐lane highway on flat road consisting of both driverless and manually driven vehicles. The driverless vehicles are equipped with fuel economy‐oriented automated controller using the PnG strategy. The manually driven vehicles are simulated using the Intelligent Driver Models (IDM) to mimic the average car‐following behavior of human drivers in naturalistic traffics. A series of simulations are conducted with three scenarios, i.e., a single car, a car section, and a car platoon. The simulation results show that the PnG strategy can significantly improve the fuel economy of individual vehicles. For traffic flows, the fuel economy and traffic smoothness vary significantly under the PnG strategy.     

Lane‐changing and freeway capacity: A Bayesian inference stochastic model

This article presents a new stochastic computational model for determining freeway capacity reduction as a result of lane‐changing activity. The probability density function for the maximum flow that can be sustained on a freeway for a given lane‐changing level is obtained. The results can be used to support freeway management strategies aiming to mitigate the negative consequences of lane‐changing in freeway capacity. A pilot test using empirical data obtained from the B‐23 freeway accessing the city of Barcelona proves the validity of the modeling approach.     

A methodology for obtaining spatiotemporal information of the vehicles on bridges based on computer vision

Spatiotemporal information of the vehicles on a bridge is important evidence for reflecting the stress state and traffic density of the bridge. A methodology for obtaining the information is proposed based on computer vision technology, which contains the detection by Faster region‐based convolutional neural network (Faster R‐CNN), multiple object tracking, and image calibration. For minimizing the detection time, the ZF (Zeiler & Fergus) model with five convolutional layers is selected as the shared part between Region Proposal Network and Fast R‐CNN in Faster R‐CNN. An image data set including 1,694 images is established about eight types of vehicles for training Faster R‐CNN. Combined with the detection of each frame of the video, the methods of multiple object tracking and image calibration are developed for acquiring the vehicle parameters, including the length, number of axles, speed, and the lane that the vehicle is in. The method of tracking is mainly based on the judgment of the distances between the vehicle bounding boxes in virtual detection region. As for image calibration, it is based on the moving standard vehicles whose lengths are known, which can be regarded as the 3D template to calculate the vehicle parameters. After acquiring the vehicles' parameters, the spatiotemporal information of the vehicles can be obtained. The proposed system has a frame rate of 16 fps and only needs two cameras as the input device. The system is successfully applied on a double tower cable‐stayed bridge, and the identification accuracies of the types and number of axles are about 90 and 73% in the virtual detection region, and the speed errors of most vehicles are less than 6%.     

Die Orinoco‐Brücke in Ciudad Guayana/Venezuela – Doppel‐Schrägkabelbrücke mit Verbundüberbau für Straßen‐ und Eisenbahngüterverkehr

The Orinoco River Bridge at Ciudad Guayana / Venezuela – a twin cable‐stayed bridge with steel composite bridge deck for road plus heavy rail traffic. The Orinoco Bridge at Ciudad Guayana carries a single track railway and a 2 × 2 lane highway across the Orinoco. The river has with low water a width of 1 km and with high water a width of 2 km. The total bridge length of 3156 m is subdivided into – the 1320 m long south approach bridge – the 1200 m long twin cable‐stayed bridge – the 636 m long north approach bridge. The bridge deck is a steel composite box girder and was erected partly by launching and partly by free cantilevering erection. The bridge is founded on large diameter bore‐piles. The paper deals with the design, special aspects of the calculation and the construction.     

Mapping to Cells: A Simple Method to Extract Traffic Dynamics from Probe Vehicle Data

In the era of big data, mining data instead of collecting data are a new challenge for researchers and engineers. In the field of transportation, extracting traffic dynamics from widely existing probe vehicle data is meaningful both in theory and practice. Therefore, this article proposes a simple mapping‐to‐cells method to construct a spatiotemporal traffic diagram for a freeway network. The method partitions a network region into small square cells and represents a real network inside the region by using the cells. After determining the traffic flow direction pertaining to each cell, the spatiotemporal traffic diagram colored according to traffic speed can be well constructed. By taking the urban freeway in Beijing, China, as a case study, the mapping‐to‐cells method is validated, and the advantages of the method are demonstrated. The method is simple because it is completely based on the data themselves and without the aid of any additional tool such as Geographic Information System software or a digital map. The method is efficient because it is based on discrete space‐space and time‐space homogeneous cells that allow us to match the probe data through basic operations of arithmetic. The method helps us understand more about traffic congestion from the probe data, and then aids in carrying out various transportation researches and applications.