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This study analyzed traffic congestion scenarios in underground roads diverging and merging sections and underground sections that run parallel to ground roads using a microscopic traffic simulation model. The appropriate ratio of the ramp inflow or outflow of traffic volume to the entry traffic volume at the starting section of the underground road network was analyzed. Furthermore, the effect of traffic operation such as adequate throughput, average traffic speed, occupancy, and volume versus capacity (V/C) ratios before the occurrence of traffic congestion in all three road networks was properly produced. To maintain a minimum level of service of all underground sections at the D–E level, the underground inflow traffic volume should be equal to approximately 70% of the 4000 pcph inflow traffic volume from an open road to the underground. The results of this study are a useful reference for establishing traffic control application standards to minimize underground traffic congestion. They also provide useful insights for underground road design. [ABSTRACT FROM AUTHOR]

The current non-connected autonomous vehicle scheme for speed changing along the road has limitations. Other alternatives require a central control or a complex communication system between the vehicles. We suggest a low cost and simple method for controlling the traffic of a line of autonomous vehicles using predetermined speed profiles imposed upon the vehicles along the road. We introduce a novel method to control autonomous vehicles traffic. Particularly, we investigate cases where specific velocities are required at some points along the road. This is done by comparing different velocity profiles for acceleration, deceleration or a combination of both. As traffic flow and speed limit may change due to upcoming road conditions, it is imperative to control vehicle line traffic, such that phantom jams will be prevented while preserving maximal traffic flow at minimum energy. We provide a comparison of these profiles for acceleration, deceleration, and for the combined case in terms of traffic flow, energy consumption, travel duration and the resulting jam characteristics. Following the comparison, we conclude that the best strategy would be to use linear speed profiles both to accelerate and to decelerate. Lastly, we suggest a tool to compare speed profiles for deceleration cases where the formation of an upstream propagated traffic congestion is inevitable. [ABSTRACT FROM AUTHOR]

This paper evaluated the congestion reduction benefits over a 6-year period from the implementation of an adaptive traffic control system (ATCS) in a congested area of Manhattan, New York, bounded by 2nd and 6th Avenues and 42nd and 57th Streets—known as the Midtown-in-Motion (MIM) area. A methodology using taxi Global Positioning System (GPS) sensors was used to measure traffic speed. Traffic speeds were calculated for May weekdays before the ATCS was installed (May 2011) and for each subsequent year (May 2012 to May 2016). Within 1 to 2 years after the implementation of the ATCS, the area’s traffic speed increased on avenues and cross streets. This gain, however, could not be sustained in subsequent years because of intervening changes in key factors impacting traffic congestion. These factors included reduction in roadway space/capacity for motor vehicles, lack of effective traffic enforcement to maintain/protect available roadway capacity, increased vehicle miles traveled (VMT) from transportation network company vehicles, and increasing volume of bicycle trips sharing street space with vehicles/pedestrians. This paper’s two key findings are, first, traffic speed gains initially seen in the MIM area after 1 year of ATCS implementation could not be sustained because intervening external factors reduced capacity and increased VMT. However, during the same analysis period, the rest of the Midtown Core area (without ATCS deployment) experienced a greater speed loss than the MIM area, indicating the effectiveness of ATCS deployment in minimizing losses in traffic speed. Second, to protect street network capacity and to minimize VMT growth, midtown Manhattan requires adopting proactive, collaborative, and coordinated strategies by the three key agencies involved with traffic management in New York City (NYC): the NYC Department of Transportation (traffic control system technology), the NYC Police Department (traffic enforcement), and the Mayor’s office (traffic demand mitigation policies). [ABSTRACT FROM AUTHOR]

Historical cities around the world have serious traffic congestions due to old infrastructure and urbanization. To mitigate traffic problems in such cities, infrastructure investments are channeled to tunnels, bridges, and highways. The solution becomes more complicated as the city centers are expected to become fully pedestrian-friendly as a European Union target for 2050. Any modification to the city transport network will lead to changes in traffic density patterns. Investment decisions become more pronounced as the speed of urbanization increases. However, this decision-making processes in urban transport networks pose a serious risk to city managers as wrong decisions are expensive and will not solve the problem. This paper proposes a practical, cost-effective model to assist decision makers by providing them with estimated changes in the traffic patterns due to the addition of new roads to existing infrastructure. The study adopts the Extreme Gradient Boosting (XGboost) which is a tree-based algorithm that provides 85% accuracy for estimating the traffic patterns in Istanbul, the city with the highest traffic volume in the world. The proposed model is a static model that allows city managers to perform efficient analyses between projects that involves changes to the city's transport network. • The study includes a traffic control model developed for a 6104 km complex network. • The study provides a productivity analysis for long-term road infrastructure investments. • The study provides an important tool for decision makers in pedestrian friendliness studies. • The study provided an important feature selection for a static traffic forecast. • The study created an application for a congested city with the most severe traffic problem. [ABSTRACT FROM AUTHOR]

This paper firstly show that 2 Dimensional Intelligent Driver Model (Jiang et al., 2014) is not able to replicate the synchronized traffic flow. Then we propose an improved model by considering the difference between the driving behaviors at high speeds and that at low speeds, which is in the framework of three-phase traffic theory. Simulations show that the improved model can reproduce the phase transition from synchronized flow to wide moving jams, the spatiotemporal patterns of traffic flow induced by traffic bottleneck, and the concave growth pattern of traffic oscillations (i.e. the standard deviation of the velocities of vehicles increases in a concave/linear way along the platoon). Validating results show that the empirical time series of traffic speed obtained from Floating Car Data can be well simulated as well. [ABSTRACT FROM AUTHOR]

A new multi-phase lattice hydrodynamic traffic flow model is proposed by considering the effect of multi-forward looking sites on a unidirectional highway. We examined the qualitative properties of proposed model through linear as well as nonlinear stability analysis. It is shown that the multi-anticipation effect can significantly enlarge the stability region on the phase diagram and exhibit three-phase traffic flow. It is also observed that the multi-forward looking sites have prominent influence on traffic flow when driver senses the relative flux of leading vehicles. Theoretical findings are verified using numerical simulation which confirms that the traffic jam is suppressed efficiently by considering the information of leading vehicles in unidirectional multi-phase traffic flow. [ABSTRACT FROM AUTHOR]

Highlights: [•] Network fundamental diagrams, based on a reduced amount of measurements, are derived. [•] Reduced NFDs exhibit a critical range of traffic states equivalent to the complete NFD. [•] Efficient feedback-based gating is possible by applying few real-time measurements. [•] Delay and mean speed improved by 30% in cases with complete and reduced measurements. [ABSTRACT FROM AUTHOR]

Abstract: It has been recently shown that a macroscopic fundamental diagram (MFD) linking space-mean network flow, density and speed exists in the urban transportation networks under some conditions. An MFD is further well defined if the network is homogeneous with links of similar properties. This collective behavior concept can also be utilized to introduce simple control strategies to improve mobility in homogeneous city centers without the need for details in individual links. However many real urban transportation networks are heterogeneous with different levels of congestion. In order to study the existence of MFD and the feasibility of simple control strategies to improve network performance in heterogeneously congested networks, this paper focuses on the clustering of transportation networks based on the spatial features of congestion during a specific time period. Insights are provided on how to extend this framework in the dynamic case. The objectives of partitioning are to obtain (i) small variance of link densities within a cluster which increases the network flow for the same average density and (ii) spatial compactness of each cluster which makes feasible the application of perimeter control strategies. Therefore, a partitioning mechanism which consists of three consecutive algorithms, is designed to minimize the variance of link densities while maintaining the spatial compactness of the clusters. Firstly, an over segmenting of the network is provided by a sophisticated algorithm (Normalized Cut). Secondly, a merging algorithm is developed based on initial segmenting and a rough partitioning of the network is obtained. Finally, a boundary adjustment algorithm is designed to further improve the quality of partitioning by decreasing the variance of link densities while keeping the spatial compactness of the clusters. In addition, both density variance and shape smoothness metrics are introduced to identify the desired number of clusters and evaluate the partitioning results. These results show that both the objectives of small variance and spatial compactness can be achieved with this partitioning mechanism. A simulation in a real urban transportation network further demonstrates the superiority of the proposed method in effectiveness and robustness compared with other clustering algorithms. [Copyright &y& Elsevier]

In this paper, a geometric model for predicting locations and times where sun glare has the potential to affect drivers on roadways was developed. Existing or prospective highway alignments can be considered. The method includes appropriate geometric models of the roadway alignment, astronomical computations for the relative position of the sun in the sky, and corrections for atmospheric refraction. Novel graphical tools were introduced to indicate the results of the model, which include sets of time and location where sun glare should be expected. The hypothesis that sun glare has an effect on congestion was tested by comparing real speed data from locations and times at which the geometric model indicates the presence of sun glare with those at which it does not. Sun glare is shown to have a statistically significant impact on mean traffic speeds. [ABSTRACT FROM AUTHOR]

The ability to measure or estimate accurate speed data are of great importance to a large number of transportation system operations applications. Estimating speeds from the widely used single inductive loop sensor has been a difficult, yet important challenge for transportation engineers. Based on empirical evidence observed from sensor data collected in two metropolitan regions in Virginia and California, this research developed a Kalman filter model to perform speed estimation for congested traffic. Taking advantage of the coexistence of dual loop and single loop stations in many freeway management systems, a calibration procedure was developed to seed and initiate the algorithm. Finally, the paper presents an evaluation that illustrates that the proposed algorithm can produce acceptable speed estimates under congested traffic conditions, consistently outperforming the conventional g-factor approach. [ABSTRACT FROM AUTHOR]

Traffic monitoring sites are installed in highways to collect speed, volume, and classification data to support various planning and intelligent transportation system applications. There is minimal research that addresses speed accuracy and methods to evaluate it without manually collecting vehicle speed data. Accurate speeds are important because the measured speed is used to calculate axle spacing distances and determine the vehicle classification. If the speed measurements are not accurate, the axle spacing distances may not conform to expected values making it difficult to classify the vehicle. This paper identifies values for the drive tandem axle spacing on Federal Highway Administration (FHwA) Scheme F Class 9 vehicles based on truck-manufacturer sales data. A relationship between the drive tandem axle spacing and speed calibration accuracy is verified by collecting and analyzing data from two weigh-in-motion (WIM) sites in Indiana. Based on this relationship, a traffic monitoring lane can be automatically calibrated by looking at historical drive tandem axle spacing measurements. This metric was utilized by the Indiana DOT to identify WIM lanes that required recalibration. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy-based methodology for quantification of congestion level for traffic control on expressways using traffic flow speed and density. Inductive loop detector data on the Interstate 880 obtained through the Freeway Performance Measurement System were used to estimate congestion levels following the fuzzy logic approach. In comparison with the Highway Capacity Manual, the results generally show a good correspondence. However, unlike the Highway Capacity Manual that defines step-wise measurement of levels of service based entirely on density, the proposed fuzzy inference system allows a flexible combination between speed and density to provide a more detailed indication of congestion intensity to describe the gradual transition of traffic state. For comparison, the congestion indices evaluated with both density and speed were compared to those evaluated with either speed or density using the same data set. Results from this comparative study reinforce the statements from previous studies that expressway speed is conservative under free-flow and light traffic conditions, but decreases significantly just before the flow rate approaches the road capacity. The results also show significant differences between the congestion indices evaluated using a single quantity, while the congestion indices using both density and speed tend to neutralize in between and scale up in a stable manner with the levels of service. Considering the abstract nature of congestion terminology, it is necessary to quantity traffic congestion on the expressways using both variables to minimize the potential bias in representing the operation of expressway traffic properly, which is particularly important under heavy congested conditions. • Single speed or density is not adequate to indicate traffic congestion. • It is necessary to quantity traffic congestion using both speed and density. • FIS flexibly combines traffic variables to better reflect traffic congestion. • FIS provides more detailed indication of congestion level. • FIS defines congestion level using words, making it easy to be perceived. [ABSTRACT FROM AUTHOR]