On the critical conditions of traffic jams

Traffic wave theory is used to study the critical conditions for traffic jams according to their features. First, the characteristics of traffic wave propagation is analyzed for the simple signal-controlled lane and the critical conditions for oversaturation is established. Then, the basic road is decomposed into a series of one-way links according to its topological characteristics. Based on the decomposition, traffic wave propagation under complex conditions is studied. Three complicated factors are considered to establish the corresponding critical conditions of jam formation, namely, dynamic and insufficient split, channelized section spillover and endogenous traffic flow. The results show that road geometric features, traffic demand structures and signal settings influence the formation and propagation of traffic congestion. These findings can serve as a theoretical basis for future network jam control.

Feedback control scheme of traffic jams based on the coupled map car-following model

Based on the pioneering work of Konishi et al. [Phys. Rev. E （1999） 60 4000], a new feedback control scheme is presented to suppress traffic jams based on the coupled map car-following model under the open boundary condition. The effect of the safe headway on the traffic system is considered. According to the control theory, the condition under which traffic jams can be suppressed is analyzed. The results are compared with the previous results concerning congestion control. The simulations show that the suppression performance of our scheme on traffic jams is better than those of the previous schemes, although all the schemes can suppress traffic jams. The simulation results are consistent with theoretical analyses.

Turning Traffic Jams to Jelly

Beijing, known for having the worst traffic in China, is brewing up a traffic plan with the harshest ever measures to ensure smooth traffic flow as the capital’s gridlock during rush hour and private car sales soar.

Will Congestion Pricing Relieve Traffic Jams?

A recent proposal by Qiu Baoxing,Vice Minister of Housing and Urban-Rural Development,to alleviate Beijing’s traffic jams has triggered heated debate among the public.

FINGDING A WAY OUT Cities carry out distinct strategies in batting with the exacerbating traffic jams

Alottery was broadcast live both on TV and over the Internet in Beijing on January 26. The "talk-of-the-town" lottery attracted at least 187,000 pairs of attentive eyes of people who were not yearning for

Characterizing Peak-Time Traffic Jam Incidents in Kampala Using Exploratory Data Analysis

Traffic congestion is associated with increased environmental pollutions, as well as reduced socio-economic productivity due to significant delays in travel times. The consequences are worse in least developed countries where motorized road transport networks are often inefficiently managed in addition to being largely underdeveloped. Recent research on traffic congestion has mostly focused on infrastructural aspects of road networks, with little or no emphasis at all on motorists’ on-the-road behavior (MB). The current study thus aimed to bridge this knowledge gap by characterizing traffic jam incidents (TJI) observed over a period of 80 days in Uganda’s Capital City, Kampala. MB as well as road network infrastructural factors such as road blockage (RB), were captured for each of the observed TJI. A total of 483 peak-time TJI were recorded, and exploratory data analysis (EDA) subsequently performed on the TJI dataset. EDA involved Hierarchical clustering analysis (HCA) and K-means clustering of the TJI dataset, as well as a detailed descriptive statistical analysis of both the entire dataset and the emerging TJI clusters. A highlight finding of this study is that 48.2% of the observed TJIs were as a result of on-the-road motorist behavior. Furthermore, the intervention of traffic police officers in a bid to regulate traffic flow was equally responsible for 25.9% of the TJIs observed in this study. Overall, these results indicate that whereas road infrastructural improvement is warranted in order to improve traffic flow, introducing interventions to address inappropriate on-the-road motorists’ behavior could alone improve traffic flow in Kampala, by over 48%. Additionally, in-order to effectively regulate traffic flow in Kampala and other least developed cities with similar traffic congestion management practices, motorists’ on-the-road behavior ought to be factored into any data-driven mechanisms deployed to regulate traffic flow and thus potentially significantly curbing traffic congestion.

Lattice models of traffic flow considering drivers' delay in response

This paper proposes two lattice traffic models by taking into account the drivers＇ delay in response. The lattice versions of the hydrodynamic model are described by the differential-difference equation and difference-difference equation, respectively. The stability conditions for the two models are obtained by using the linear stability theory. The modified KdV equation near the critical point is derived to describe the traffic jam by using the reductive perturbation method, and the kink-antikink soliton solutions related to the traffic density waves are obtained. The results show that the drivers＇ delay in sensing headway plays an important role in jamming transition.

Travel time prediction with viscoelastic traffic model

Travel time through a ring road with a total length of 80 km has been predicted by a viscoelastic traffic model(VEM), which is developed in analogous to the non-Newtonian fluid flow. The VEM expresses a traffic pressure for the unfree flow case by space headway, ensuring that the pressure can be determined by the assumption that the relevant second critical sound speed is exactly equal to the disturbance propagation speed determined by the free flow speed and the braking distance measured by the average vehicular length. The VEM assumes that the sound speed for the free flow case depends on the traffic density in some specific aspects, which ensures that it is exactly identical to the free flow speed on an empty road. To make a comparison, the open Navier-Stokes type model developed by Zhang(ZHANG, H. M. Driver memory, traffic viscosity and a viscous vehicular traffic flow model. Transp. Res. Part B, 37, 27–41(2003)) is adopted to predict the travel time through the ring road for providing the counterpart results.When the traffic free flow speed is 80 km/h, the braking distance is supposed to be 45 m,with the jam density uniquely determined by the average length of vehicles l ≈ 5.8 m. To avoid possible singular points in travel time prediction, a distinguishing period for time averaging is pre-assigned to be 7.5 minutes. It is found that the travel time increases monotonically with the initial traffic density on the ring road. Without ramp effects, for the ring road with the initial density less than the second critical density, the travel time can be simply predicted by using the equilibrium speed. However, this simpler approach is unavailable for scenarios over the second critical.

Traffic jam in signalized road network

Traffic jam in large signalized road network presents a complex nature.In order to reveal the jam characteristics,two indexes,SVS(speed of virtual signal) and VOS(velocity of spillover),were proposed respectively.SVS described the propagation of queue within a link while VOS reflected the spillover velocity of vehicle queue.Based on the two indexes,network jam simulation was carried out on a regular signalized road network.The simulation results show that:1) The propagation of traffic congestion on a signalized road network can be classified into two stages:virtual split driven stage and flow rate driven stage.The former stage is characterized by decreasing virtual split while the latter only depends on flow rate; 2) The jam propagation rate and direction are dependent on traffic demand distribution and other network parameters.The direction with higher demand gets more chance to be jammed.Our findings can serve as the basis of the prevention of the formation and propagation of network traffic jam.

An extended master-equation approach applied to aggregation in freeway traffic

We restudy the master-equation approach applied to aggregation in a one-dimensional freeway, where the decay transition probabilities for the jump processes are reconstructed based on a car-following model. According to the reconstructed transition probabilities, the clustering behaviours and the stochastic properties of the master equation in a one-lane freeway traffic model are investigated in detail The numerical results show that the size of the clusters initially below the critical size of the unstable cluster and initially above that of the unstable cluster all enter the same stable state, which also accords with the nucleation theory and is known from the result in earlier work. Moreover, we have obtained more reasonable parameters of the master equation based on some results of cellular automata models.

Jam Formation of Traffic Flow in Harbor Tunnel

This paper reports a study concerning occurrence and growth of traffic jam in a harbor tunnel. The single-lane with three sections （downgrade, fiat, and upgrade） is taken into account and they are characterized with different velocity limit. At the low density, the traffic current increases linearly with density and saturates at some values of immediately density. As the density increases, the traffic jam appears firstly before the upgrade section and then extends to the downgrade section. Additionally, the relationships of the velocity and headway against position in different densities are obta/ned from simulation. These results clearly clarify where and when the traffic jam appears. Finally, the critical densities are derived via the theoretical analysis before and after the discontinuous fronts and the theoretical results are consistent with the critical values of simulation results.

Time Prediction Through A Congested Road Section

First,the cellular automaton was used to simulate a"T"junction,and the correlation analysis was performed by combining the traffic pattern and the corresponding data to obtain the reason for the inaccurate prediction time of the navigation software.The collected data is preprocessed to obtain the driving time of multiple road vehicles in a week,and this is used as the influencing factor.Reuse the collected information:the length of the intersection,the average speed of real-time vehicles at the intersection,and the length of the intersection.The first two processes of the three pre-processing processes are considered together to obtain a time-dependent factor.The correlation factors and the duration of the intersections are used to predict the results of neural network training.Based on the analysis and prediction of the data,the causes of urban traffic congestion are analyzed,and measures to reduce urban congestion are proposed.

Predicting effects of tunnel throttling of annular freeway vehicular flow by a continuum model

Fluid flow throttling is common in industrial and building services engineering.Similar tunnel throttling of vehicular flow is caused by the abrupt number reduction of roadway lane,as the tunnel has a lower lane number than in the roadway normal segment.To predict the effects of tunnel throttling of annular freeway vehicular flow,a three-lane continuum model is developed.LaneⅢof the tunnel is completely blocked due to the need of tunnel rehabilitation,etc.There exists mandatory net lane-changing rate from laneⅢto laneⅡjust upstream of the tunnel entrance,which is described by a model of random number generated through a golden section analysis.The net-changing rate between adjacent lanes is modeled using a lane-changing time expressed explicitly in algebraic form.This paper assumes that the annular freeway has a total length of 100 km,a two-lane tunnel of length 2 km with a speed limit of 80 km/h.The free flow speeds on lanesⅠ,ⅡandⅢare assumed to be 110,100 and 90 km/h respectively.Based on the three-lane continuum model,numerical simulations of vehicular flows on the annular freeway with such a tunnel are conducted with a reliable numerical method of 3rd-order accuracy.Numerical results reveal that the vehicular flow has a smaller threshold of traffic jam formation in comparison with the case without tunnel throttling.Vehicle fuel consumption can be estimated by interpolation with time averaged grid traffic speed and an assumed curve of vehicle performance.The vehicle fuel consumption is lane number dependent,distributes with initial density concavely,ranging from 5.56 to 8.00 L.Tunnel throttling leads to an earlier traffic jam formation in comparison with the case without tunnel throttling.

Impact of the Next-Nearest-Neighbor Interaction on Traffic Flow of Highway with Slopes

In this paper,we study the motion course of traffic flow on the slopes of a highway by applying a microscopic traffic model,which takes into account the next-nearest-neighbor interaction in an intelligent transportation system environment.Three common gradients of the highway,which are sag terrain,uphill terrain,and downhill terrain on a single-lane roadway,are selected to clarify the impact on the traffic flow by the next-nearest-neighbor interaction in relative velocity.We obtain the current-density relation for traffic flow on the sag,the uphill and the downhill under the next-nearest-neighbor interaction strategy.It is observed that the current saturates when the density is greater than a critical value and the current decreases when the density is greater than another critical value.When the density falls into the intermediate range between the two critical densities it is also found that the oscillatory jam,easily leads to traffic accidents,often appears in the downhill stage,and the next-nearest-neighbor interaction in relative velocity has a strong suppressing effect on this kind of dangerous congestion.A theoretical analysis is also presented to explain this important conclusion.

Module 2 Traffic Jam

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