Investigating the random behavior of the practical capacity of the highway and its effect on ramp control

Dissertation for master's degree

Civil Engineering - Transportation Planning

Abstract

Ramp control is one of the most effective highway management strategies and has many benefits in terms of increasing output at bottlenecks, reducing travel time, number of accidents and so on. has created Ramp control consists of two competing constraints: Highway capacity and ramp queue. The implementation and evaluation of ramp control shows that these two restrictions cannot work successfully due to the incorrect assumption of constant capacity and incorrect estimation of queue length. In this thesis, two improved methods are proposed to solve these problems in the capacity discussion. The first method is an estimate of current capacity that is suitable for addressing capacity deficiencies that are assumed to be constant. When congestion starts, the use of time-varying practical capacity is recommended. This issue is calculated by moving average method based on momentary traffic. The hybrid method was tested on the stratified area control strategy [1] through AIMSUN microscopic simulation software. The second method is a proposed strategy for ramp control, by limiting the probability. This was done first by studying the probability density function for the capacity in different flow conditions during the period. Investigations showed that the distribution of instantaneous capacity within the range of occupancy and density is in the form of a normal probability distribution. In this method, the amount of capacity changes dynamically based on the current traffic conditions and the acceptable probability of the determined risk level. In order to evaluate the efficiency of dynamic random capacity constraint, a methodology for ZONE algorithm was used and tested through microscopic simulation.

Keywords: Practical highway capacity, ramp control, probability limit, stochastic behavior

Chapter 1

Research overview

   1-1 Introduction

   Population growth and employment have been accompanied by reliance on cars and highway systems as the main means of urban mobility, this issue has brought a huge responsibility in the transportation infrastructure sector. Due to urban development, road inaccessibility and environmental restrictions, adding more lanes or building additional highways are not long-term solutions. Instead, effective management strategies are preferred in the developing highway to maximize the use of existing infrastructure.

    A ramp can be defined as a connection between two highway facilities that includes a section of road of sufficient length to ensure the safety of vehicles connecting from the entrance ramp to the main road. As the demand of vehicles on the entrance ramp increases, uncertain situations arise to join the busy highway. To control the added demand to the input ramp, several ramp control strategies [1] have been developed by limiting the number of devices entering the mainstream. Ramp control, one of the most effective highway management strategies, has long been able to produce many benefits for the public. Things such as increasing the amount of output [2] in bottlenecks [3], reducing travel time, improving the reliability of travel time and reducing the number of accidents as well as the emission of vehicle pollution (Cambridge Systmatics, 2001). In this method, ramp counters in the form of controlling traffic signs that are installed on the entrance ramps of freeways and highways control the amount of cars that enter the main line, so that the amount of downstream flow increases. It is possible to transfer the maximum flow of traffic at a uniform speed. Ramps are involved in the discharge of traffic at a measured rate based on instantaneous traffic conditions, thus avoiding the violation of the sensitive demand-capacity balance on the main road. On the other hand, ramp controls regulate the ramp traffic by breaking up the incoming groups of vehicles in order to reduce the disturbance in the converging areas. As a result, side and rear crashes resulting from restricted ramp access are reduced. However, control ramps have the potential to create long queues that may block[8] flow from the upstream ramp and disrupt street level performance..

1-2 Problem Description

   An effective and successful ramp control strategy generally improves and balances the relationships between main line flow and vehicle waiting time and queuing at entrance ramps. Therefore, the two constraints in ramp control conflict are highway capacity and ramp queue. An example of a currently implemented strategy is stratified zone control (SZM). In this strategy, the capacity limitation has been investigated. Considering this limitation, on the one hand, maintains the balance between the demand capacity on the highway; On the other hand, the delay keeps the maximum ramp under predetermined boundary conditions as high as possible. However, the implementation and evaluation of ramp control strategies show that this constraint cannot work satisfactorily for the following reasons. First, it is assumed that the capacity of the highway is fixed and predetermined. Fixed capacity is sufficient for some applications such as highway design and planning, but not suitable for instantaneous highway operation such as ramp control. The value of fixed capacity is often greater than the value of capacity in prevailing conditions. This issue will lead to high congestion [9] on the highways, which will occur due to the excessive release of vehicles from the ramps. On the other hand, the fixed capacity value lower than the actual value will lead to long queues in the ramp. Second, there is no accurate method to estimate the length of the queue at the ramp. Usually, a single pre-calibrated regression equation is used to estimate queue length at all ramps. This is a case for the current SZM strategy. Detailed evaluation (Liu et al., 2007) shows that sometimes the queue length estimation model shows a lower value than the actual value, which will lead to an increase in waiting time[10]. In other ramps where the length of the queue is considered higher than reality, the result will be the release [11] of more vehicles on the main road and acceleration of the onset of congestion. The proposals are based on an improved design for the freeway capacity estimation method, which calculates the capacity variably based on the prevailing freeway conditions, especially when a section is congested. In the first method, a simple method for estimating instantaneous capacity is presented, which is generally suitable for instantaneous control and programs related to dealing with common deficiencies due to the constant assumption of capacity. In the second method, based on these findings that the instantaneous capacity for a wide range of occupancy level [12] and density follows a normal distribution [13], a new ramp control strategy with probability limit [14] has been proposed, in which the amount of capacity changes dynamically depending on the current traffic conditions and finally the acceptable probability of the determined risk level is presented [15]. A solution for this type of probability constraint programs is general and should be applicable to various control programs as well. These methods have been implemented in ZONE and SZM and evaluated through microscopic simulation. 1-3 Research Objectives The research described in this thesis pursues the following objectives: Clear definition of practical capacity[16] under variable traffic flow conditions and development of improved highway capacity estimation methods based on variable capacity. And also the sudden drop[17] of capacity when the congestion starts.

To investigate the stochastic behavior[18] of the practical capacity of the highway

Setting the ramp control strategy with probability constraint conditions

Show the application of the proposed methods through implementation with a specific strategy, and evaluate the effectiveness using microscopic traffic simulation.

1-4 Research background and research importance

Investigating the random behavior of the practical capacity of the highway and its effect on ramp control is a relatively new topic. The strategy used here is determined based on real-time traffic data on the highway in order to determine the control policy.

- The use of the ramp control method as one of the traffic management solutions on highways or freeways started in 1960 in the cities of Detroit, Chicago and Los Angeles.

- Previous studies include field studies and simulation studies.

- The idea of ??using the ZONE algorithm and applying capacity restrictions. It has been used in many researches such as (Lau, 1996).

- In HCM 2000, the stochastic behavior of capacity is mentioned.

- Brilon W