Real time queue length estimation for signalized intersections using travel times from mobile sensors

Input-queued scheduling algorithms are designed to improve the throughput which use the input and output terminal in the router to obtain the maximum packets. And the length of the input queue is an important target, that can decides the superiority-inferiority about the capability of the input algorithm. With the existing algorithms, the main content about the scheduling algorithm are ensuring the packets’ high priority and low level. But most of these algorithms do not consider the length of queue. In this paper, we consider the change of queue length when the packets enter the input channel at the router, and put forward a shortest queue time-slot adapting algorithm (SQTA algorithm). First of all, SQTA algorithm can base the changes of transmittion time to control the packets’ sending time interval, and this action can make SQTA algorithm match the processing power about the router, and at the same time, this algorithm can reduce the length of the packets queue. Second, SQTA algorithm can control of a phenomenon that the packets may overflow when the length of queue is full, and that also make the packets transmitted more stable in network. The experimental result shows that this scheduling algorithm can control the length of queue effectively and reduce the waiting time with packets queue, It can be shorten the remaining service time, and can improve the network operation obviously.

The feedback control method proposed in this paper predicts the queue length in the switch using the slope of queue length prediction function and queue length changes in time-series. The predicted congestion information is backward to the node. NLMS and neural network are used as the predictive control functions, and they are compared from performance on the queue length prediction. Simulation results show the efficiency of the proposed method compared to the feedback control method without the prediction. Therefore, we conclude that the efficient congestion and stability of the queue length controls are possible using the prediction scheme that can resolve the problems caused from the longer delays of the feedback information.

The feedback control method proposed in this paper predicts the queue length in the switch using the slope of queue length prediction function and queue length changes in time-series. The predicted congestion information is backward to the node. NLMS and neural network are used as the predictive control functions, and they are compared from performance on the queue length prediction. Simulation results show the efficiency of the proposed method compared to the feedback control method without the prediction. Therefore, we conclude that the efficient congestion and stability of the queue length controls are possible using the prediction scheme that can resolve the problems caused from the longer delays of the feedback information.

Queue length is an important index of the efficiency of urban transport system. The traditional approaches seem insufficient for the estimation of the queue length when the traffic state fluctuates greatly. In this paper, the problem is solved by introducing the Cell Transmission Model, a macroscopic traffic flow, to describe the vehicles aggregation and discharging process at a signalized intersection. To apply the model to urban traffic appropriately, some of its rules were improved accordingly. Besides, we can estimate the density of each cell of the road in a short time interval. We, first, identify the cell, where the tail of the queue is located. Then, we calculate the exact location of the rear of the queue. The models are evaluated by comparing the estimated maximum queue length and average queue length with the results of simulation calibrated by field data and testing of queue tail trajectories. The results show that the proposed model can estimate the maximum and average queue length, as well as the real-time queue length with satisfactory accuracy.

With the development of intelligent transportation system (ITS) and vehicle to X (V2X), the connected vehicle is capable of sensing a great deal of useful traffic information, such as queue length at intersections. Aiming to solve the problem of existing models’ complexity and information redundancy, this paper proposes a queue length sensing model based on V2X technology, which consists of two sub-models based on shockwave sensing and back propagation (BP) neural network sensing. First, the model obtains state information of the connected vehicles and analyzes the formation process of the queue, and then it calculates the velocity of the shockwave to predict the queue length of the subsequent unconnected vehicles. Then, the neural network is trained with historical connected vehicle data, and a sub-model based on the BP neural network is established to predict the real-time queue length. Finally, the final queue length at the intersection is determined by combining the sub-models by variable weight. Simulation results show that the sensing accuracy of the combined model is proportional to the penetration rate of connected vehicles, and sensing of queue length can be achieved even in low penetration rate environments. In mixed traffic environments of connected vehicles and unconnected vehicles, the queuing length sensing model proposed in this paper has higher performance than the probability distribution (PD) model when the penetration rate is low, and it has an almost equivalent performance with higher penetration rate while the penetration rate is not needed. The proposed sensing model is more applicable for mixed traffic scenarios with much looser conditions.

This article presents methods to estimate the location of a vehicle in the queue based on the vehicle's travel time traversing a signalized intersection. The methods focus on the queue discharging process, and calculate the location (in the queue) and acceleration rate of the vehicle simultaneously using kinematic equations. If such location information can be calculated for multiple sampled vehicles in a cycle, the dynamic profile of queue length of the cycle can be estimated. By using travel times, the methods can be applied to data collected via many newly emerged traffic data collection systems such as mobile sensors. The proposed methods are tested using data from simulation, a field experiment, and the Next Generation Simulation (NGSIM), and are compared with a previously developed queue length method.

Transportation problems in the form of congestion, delays, as well as air and noise pollution are very common in several big cities in Indonesia such as Denpasar City (the capital of Bali Province). Traffic congestion has been experienced on almost all major roads in Denpasar City and one of them is Sudirman road. There are several signalized intersections located at close distances. Traffic jam is usually experienced during peak hour which causes an increase in travel time, energy consumption, and air pollution. There is a potential to coordinate these intersections in order to reduce congestion. The objective of this study is to optimize intersection performances by coordinating traffic signals. This study applied a micro-simulation approach by using Vissim software. The t-test method was used to test the validity of the simulation. The results indicated that the intersections were in the level of service F. Comparison of the intersection performances showed that there was an increase in the intersection performances as indicated by the decrease in the queue length, delay, stop delay, and fuel usage. There was a decrease in the queue length of about - 18.52% (from 544.63 m to 443.74 m), a decrease in the delay of about - 20.41% (from 344.19 sec/pcu to 273.94 sec/pcu), a decrease in the stop delays of about -21.61% (from 32.84 sec/pcu to 25.74 sec/pcu) and a decrease in the fuel use of about -5.15% (from 17.19 liters/hour to 16.30 liters/hour).

During the past several decades significant changes in travel behavior in the United States have occurred. Evidence from the Nationwide Personal Transportation Survey series and the 2001 National Household Travel Survey (NHTS) indicates that the average daily travel time per person has increased by 1.9 min per year between 1983 and 2001. The objective of this paper is to explore the growth of daily travel time expenditures. Changes in society, technology, incomes, attitudes, and sociodemographic and household structure have been hypothesized as having contributed to the travel time growth. This analysis explores those variables and their relationship to increases in travel time. Aggregate values are used to investigate the relationships between daily travel time expenditures and sociodemographic characteristics. This paper comments on the share of travel time growth that can be explained by the available variables and speculates on the implications of other factors on travel time expenditure growth. A review of the NHTS data set and an analysis of the relationships between the data set and travel time expenditures make clear that travel budget changes that appear to be related to the set of available variables do not explain the full reason for the significant increase in travel time spending. The increases are across all market segments: age, income, gender, ethnicity, household composition, and so on. Thus, shifts in demographic or other traveler conditions do not fully explain the increases in trip making. That has a significant implication for transportation planning–-traditional sociodemographic predictors for trip making do not appear to be sufficiently causal to be useful for understanding current and future factors influencing trip making and travel expenditure changes.

This paper presents a new method to estimate real-time queue lengths at a signalized intersection by utilizing limited data extracted from surveillance videos. This method focuses on reconstructing vehicles' trajectories on an entire road segment. The real-time queue length can be derived from these reconstructed trajectories. In order to improve the accuracy of the trajectory reconstruction, a built-up car-following model is proposed to reconstruct the trajectories of vehicles joining and leaving the queue, respectively, which are further corrected by a fusion algorithm. The proposed method was validated in the Next Generation Simulation dataset, and the queue length for each signal cycle can be estimated with a high accuracy. The results show that the proposed method has a higher precision compared to three baseline models.

Energy optimization in freight train operations: Algorithmic development and testing

Past studies on automatic traffic incident detection have mainly focused on the incidents on freeways, which are controlled-access roads. There are not many works on urban traffic incident detection. In addition, the traffic data used in detecting an incident are still mostly collected from fixed sensors such as loop detectors. With the advances in mobile sensing and vehicular technology, it is foreseeable that mobile sensors will be used increasingly in the near future. In fact, traffic data will be collected directly by vehicles. Detecting traffic incidents in an urban road network with the traffic data collected by mobile sensors poses several challenges. First, the urban roads are uncontrolled-access roads, which are typically full of flow-disruptive entities such as traffic signals, intersections, crossings, bus stops, etc. These entities can disrupt the traffic flow in a similar way that an incident does, making it more difficult to detect an incident. Second, it is still not clear which traffic variables, collected by mobile sensors, can be used in detecting an incident in an urban environment. In this paper, we investigate and identify the traffic variables that are effective in detecting an incident in an urban road network. Particularly, speed, acceleration, lane-change ratio and travel time are studied. The results show that these four traffic variables are generally effective for traffic incident detection. However, among the four variables, the least effective one is the travel time.

At intersections, Left-turning vehicles seek to occupy the same physical space as close to the stop line as possible. These result in high conflicts, delays, and blockage of vehicles by turning vehicles and vice versa. The impact of the lengths of Left-turn lanes on intersection delays is considered to optimize the lengths of the Left-turn lanes. Data for traffic counts, queue lengths, and signal timing are collected from three intersections in Baghdad city in Iraq. The methodology involves the development of estimation models using traffic Simulation SIDRA INTERSECTION 8.0 and simulating various scenarios by varying traffic signal conditions to evaluate delays and queues caused by varying lengths of the Left-turn Lane. Optimal lengths are computed and compared to existing lengths in intersections. No differences in delay, queue, and lengths of the Left turn lane are found using t-test analysis for significance. Outputs from the three models were compared to the maximum observed in the field from the selected intersections. Data analysis involved determining the R2 and the standard error mean between the model output and the observed data. In general, SIDRA INTERSECTION 8.0 overestimated queue vehicles and length of storage for approaches with a high degree of saturation ratios and underestimated it for those with a high degree of saturation ratios.

Investigating the nonlinear relationship between transportation system performance and daily activity–travel scheduling behaviour

Vehicle travel time on arterial roads is a crucial parameter for traffic management and traveler information systems. A travel time distribution is an effective way to represent the essential properties of this parameter. This paper proposes a modified Gaussian Mixture Model for representing travel time distributions on arterial roads with signalized intersections. The proposed model is applicable to travel time data from both fixed and mobile sensors. The performance of the model was evaluated using real travel time measurements from fixed sensors in the field and virtual mobile sensor data generated from those real-world measurements. The evaluation results show very good performance of the model in representing the traffic state on arterial roads. The model can be applied to historical datasets to estimate the amount of stop time and non-stop time for vehicles on arterial links during a specific time period, which is useful information for a variety of traffic applications, such as arterial travel time prediction and arterial traffic energy/emission estimation.

BackgroundTravel time to treatment facilities may impede the receipt of guideline-concordant treatment (GCT) among patients diagnosed with early-stage non-small cell lung cancer (ES-NSCLC). We investigated the relative contribution of travel time in the receipt of GCT among ES-NSCLC patients.MethodsWe included 22,821 ES-NSCLC patients diagnosed in California from 2006–2015. GCT was defined using the 2016 National Comprehensive Cancer Network guidelines, and delayed treatment was defined as treatment initiation >6 versus ≤6 weeks after diagnosis. Mean-centered driving and public transit times were calculated from patients’ residential block group centroid to the treatment facilities. We used logistic regression to estimate risk ratios and 95% confidence intervals (CIs) for the associations between patients’ travel time and receipt of GCT and timely treatment, overall and by race/ethnicity and neighborhood socioeconomic status (nSES).ResultsOverall, a 15-minute increase in travel time was associated with a decreased risk of undertreatment and delayed treatment. Compared to Whites, among Blacks, a 15-minute increase in driving time was associated with a 24% (95%CI = 8%-42%) increased risk of undertreatment, and among Filipinos, a 15-minute increase in public transit time was associated with a 27% (95%CI = 13%-42%) increased risk of delayed treatment. Compared to the highest nSES, among the lowest nSES, 15-minute increases in driving and public transit times were associated with 33% (95%CI = 16%-52%) and 27% (95%CI = 16%-39%) increases in the risk of undertreatment and delayed treatment, respectively.ConclusionThe benefit of GCT observed with increased travel times may be a ‘Travel Time Paradox,’ and may vary across racial/ethnic and socioeconomic groups.