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To alleviate traffic congestion, an improved car-following model for connected vehicles is proposed in this paper by considering backward-looking effect and motion information of multiple vehicles. The backward-looking effect is common in traffic and motion information of multiple vehicles and is beneficial for alleviating traffic congestion. The linear and nonlinear stability of the proposed model is analyzed, and the stability condition and modified Korteweg-de Vries equations of the proposed model are derived. The theoretical analysis results prove the effectiveness of the proposed model in alleviating traffic congestion and improving the stability of the traffic system. Further, numerical simulation is designed to verify the promoting effect of the introduced parameters on the traffic stability and to test the effect of the improved model on large-scale car-following queues. Finally, the next-generation simulation (NGSIM) data sets are used to calibrate the parameters of the improved model. The results show that the improved model can effectively avoid traffic congestion and enhance the stability of traffic flow. The improved model can be used as active safety technology to prevent collision accidents or as a car-following strategy in driverless algorithms. [ABSTRACT FROM AUTHOR]

Maintaining roadway pavements and bridge decks is key to providing high levels of service for road users. However, improvement actions incur downtime. These actions are typically scheduled by asset class, yet implemented on any asset type, they have network-wide impacts on traffic performance. This paper presents a bilevel program wherein the upper level involves a Markov decision process (MDP) through which potential roadway improvement actions across asset classes are prioritized and scheduled. The MDP approach considers uncertainty in component deterioration effects, while incorporating the benefits of implemented improvement actions. The upper level takes as input traffic flow estimates obtained from a lower-level user equilibrium traffic formulation that recognizes changes in capacities determined by decisions taken in the upper level. Because an exact solution of this bilevel, stochastic, dynamic program is formidable, a deep reinforcement learning (DRL) method is developed. The model and solution methodology were tested on a hypothetical problem from the literature. The importance of obtaining optimal activity plans that account for downtime effects, traffic congestion impacts, uncertainty in deterioration processes, and multiasset classes is demonstrated. [ABSTRACT FROM AUTHOR]

In this paper, the authors present their efforts in exploring a new type of traffic data, referred to as internet-connected vehicle (ICV) data, for traffic congestion management and operational planning. Most currently manufactured vehicles contain onboard GPS and cellular modules, and they constantly connect to automobile manufacturers' clouds via cellular networks and upload their status. Some automobile manufacturers have recently redistributed the nonpersonal part of such data, such as geolocation, to third-party organizations for innovative applications. Compared with the traditional vehicle GPS data, the ICV data contain high-resolution GPS waypoints accompanied with the vehicles' abnormal moving events (e.g., hard braking). The ICV data also have huge potential in congestion management and operational planning. They explore to identify and analyze traffic congestion on both freeways and arterials using the ICV data. The ICV data adopted for this research are redistributed by Wejo Data Service, representing 10%–15% of all moving vehicles in the Dallas–Fort Worth (DFW) area in Texas. Through one case study for a freeway segment and one for an arterial segment, new traffic performance metrics based on the characteristics of ICV data have been presented. The highlights of these efforts are as follows: (I) queue length and propagation at freeway bottlenecks can be directly measured based on where and when most internet-connected vehicles slow down and join the queue; (II) an internet-connected vehicle's actual delay time on arterials can be directly measured according to its slow movement percentage, without assuming the nondelay travel speed; and (III) the ICV data set are also combined with the high-resolution traffic signal events to generate a ground-truth time-space diagram (TSD) on arterials—a common visualization of arterial signal performance for transportation planning and operations. [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]

Comprehensive stated choice (SC) experiments were conducted in Jakarta, Indonesia, and Nagoya, Japan, where proposals for congestion charges have been introduced and remain under consideration as a way to reduce acute car dependence, particularly in Jakarta. Causal paths among psychological determinants and their strengths are measured and analyzed along with proposal acceptability from a cross-country perspective based on the similar context of the SC experiments. The findings from analysis with a multiple-sample, multipleindicator, multiple-cause (MS-MIMIC) model show that a number of psychological determinants provide an explanation for the acceptability of the proposed scheme in both cities. Psychological motivations, including awareness of the city's environment and awareness of the problem of cars in society, appear to be the most important direct determinants leading to recognition of the effects of a congestion charging scheme and they are indirect determinants of policy acceptance in both cities. However, the proposed scheme is found to be more correct and acceptable in Jakarta. Findings from analysis further discloses that Nagoya is slightly more car dependent than Jakarta even though congestion is recognized worse in Jakarta. The effect of the specific measure indicator trust in government policy on the perception of correct and acceptable policies is investigated, revealing a negative determinant for Nagoya and vice versa for Jakarta. This indicates the important role of current government performance for achieving acceptability for these proposals. Moreover, findings from analysis with a hybrid discrete choice (HDC) model further suggest that tangible determinants, such as charge scenarios and individual mobility attributes can be a barrier to acceptance in both cities, along with the intangible determinant of inhibition of freedom of movement (IFM). On the other hand, the key intangible determinants recognition of the scheme's effects (REC) and trust in government policy (TGP) might enhance acceptability of the scheme in Jakarta, while TGP may form a considerable barrier in the case of Nagoya. [ABSTRACT FROM AUTHOR]

The impacts of road congestion charges on traffic safety are often overlooked in evaluating the benefits of congestion charging practices and searching for traffic safety strategies. This paper aims to examine how congestion pricing affects both efficiency and automobile safety. The first part of the study proposes and formulates the general traffic accident minimization pricing (TAMP) problem. Considering within-day and day-to-day travel demand fluctuations, a multiobjective kbilevel optimization model under stochastic travel demand is developed based on the TAMP problem. This model is employed to determine the optimal toll levels that minimize both the negative externalities of congestion and accidents. Numerical results indicate that the traditional efficiency-oriented congestion pricing scheme, which does not consider impacts to safety, is often implemented in a less beneficial manner with regard to accident reduction. [ABSTRACT FROM AUTHOR]

This paper examines the value of utilizing dynamic, revenue-maximizing, congestion pricing on a privately operated tolled route, when the only alternative is a highly congested, public, free-access route. Three methods are specified by which revenue can be maximized-through the selection of a fixed percent of users to set the toll price for, through the selection of a fixed level of service on the toll road, and through the use of a nonlinear optimization model. These methods are tested on a case study involving a highly congested corridor. In simulation, the corridor’s flow rates are observed following the posting of a toll, the toll is updated, and the new flow observed until a subsequent round of revenue maximizing is applied to the new conditions. It was concluded that in a highly congested corridor a dynamic, revenue-maximizing toll can finance the construction of new capacity without degrading social welfare. [ABSTRACT FROM AUTHOR]

This paper presents a solution methodology that can be used to determine the optimal solution for the combined congestion pricing and capacity expansion problems. A bilevel genetic algorithm (GA)-based optimization solution methodology is proposed to determine the optimal toll location, toll rate, percentage capacity expansion, and location for the expansion simultaneously. The upper-level subprogram minimizes the total system travel cost given certain budget and toll constraints. The lower-level subprogram is a user equilibrium problem where all users try to find the route that minimizes their own travel cost (or time). The budget constraint is handled using a penalty parameter. The demand is assumed to be fixed and given a priori. The proposed GA model is applied to Sioux Falls network, which has 76 links and 24 origin-destination (OD)-pairs, assuming homogeneous users. The optimal solution is thus identified. Sensitivity analyses are conducted for the budget and penalty parameter. The proposed methodology will be a very useful tool for transportation network planners for allocation of budgets and prioritization of links for improvements and congestion pricing. [ABSTRACT FROM AUTHOR]

Demonstrates the computation of impacts associated with alternative methods of maintaining traffic during construction. Computation of equivalent average vehicle routes for complex diversion routes; Documentation of vehicle and route inputs; User cost for individual trips through the work zone.

Describes the underlying construction congestion cost system model of traffic demand, delay and user cost. Focus on methods used for calculating traffic backup and delay, diverted and canceled trips and road user cost; Variation of road capacity during construction work.

Worsening highway congestion is a challenge to megaregional competitiveness; changing regional geographies and development location decisions, among other factors, demand that public policy responses go beyond traditional demand-management approaches. Congestion-pricing has been suggested as a remedy. In this paper, the writers analyze the outcomes of multiple congestion-pricing approaches for the capital megaregion that spans the following five metropolitan planning organization regions: (1) Washington (District of Columbia, Maryland, and Virginia), (2) Baltimore (Maryland), (3) Wilmington (Delaware), (4) Fredericksburg (Virginia), and (5) Frederick (Maryland), in addition to counties in the adjoining states of New Jersey, Pennsylvania, and West Virginia in the United States. Using a megaregional travel-demand model, the writers incorporate different values-of-time (VOTs) for travelers under different conditions. However, unlike past approaches, the writers' VOT estimates go beyond income categories and include trip purposes across a number of scenarios. The writers demonstrate that adding the trip purpose to congestion-price determination provides added insight both at the megaregional level and for individual subregions. The results of this paper show how highway users respond differently and (in some cases) in unexpected ways to congestion charges when trips in accordance with income and purpose are assigned a unique VOT. The findings reported in this paper provide engineers, planners, and policymakers with a new analytical approach to measuring traveler elasticity to tolls while demonstrating the complexity involved with road-pricing mechanisms. The writers conclude with implications for adopting this approach and ideas for implementing them in a complex institutional setup. [ABSTRACT FROM AUTHOR]