Due to topography, geology, and other factors, small spacing sections are common between tunnels and interchange exits. There is mandatory lane-changing behavior for vehicles that need to leave the main line and drive inside the road before leaving the tunnel. Affected by the “white hole” of a tunnel, the lane-changing behavior of off-ramp vehicles differs significantly from that of original roadbed sections. To study the mandatory lane-changing duration (MLCD) of off-ramp vehicles in small spacing sections of the tunnel to interchange in mountainous areas, their time and trajectory data were collected based on a driving simulator. According to the characteristics of the data, the survival analysis method was used to analyze the influence on the MLCD of off-ramp vehicles of the spacing section between the tunnel and interchange, vehicle types, tunnel types, ramp types, highway service level, and whether to set exit advance guide signs in the tunnel and the Cox proportional hazards model of the MLCD was established. The results showed that the spacing of the tunnel interchange, the road service level, and whether to set exit advance guide signs in the tunnel had significant effects on the MLCD of vehicles, while the vehicle, the tunnel, and the ramp types did not. When the spacing section of the tunnel interchange was less than 500 m, the off-ramp vehicle had continuous mandatory lane-changing behavior, and when the distance decreased from 400 m to 300 m, the risk rate of lane changing increased by 5.68 times. Survival function curve estimation provided the 75% quantile of MLCD of off-ramp vehicles under different conditions, which could provide a theoretical reference for setting the minimum distance between a tunnel and interchange exit.

Off-Ramp Vehicle Mandatory Lane-Changing Duration in Small Spacing Section of Tunnel-Interchange Section Based on Survival Analysis

Freeway Traffic Control in the Presence of Capacity Drop

In this research study, a multistage fuzzy logic controller (MS-FLC) was developed for traffic control in incident management on expressways. The MS-FLC serves as the traffic operator’s decision-making support tool at the operational level. The MS-FLC gathers real-time traffic and incident data to analyze and predict traffic conditions, as well as to recommend alternative control measures to the traffic operator in the form of linguistic expressions. The MS-FLC is embedded in a traffic simulator controller (TSC) prototype, and was evaluated by comparing its performance with no control scenario and ALINEA\Q, a popular local ramp control algorithm, across several incident scenarios in a simulation environment. In general, MS-FLC outperformed ALINEA\Q with respect to global objectives. In particular, whereas the ALINEA\Q algorithm favors the mainline, the MS-FLC algorithm significantly improves mainline travel conditions while substantially reducing ramp queues. It is concluded that, if properly designed, the MS-FLC can be a robust tool for traffic control on expressways under incident conditions. 

https://purehost.bath.ac.uk/ws/files/228167641/MS_FLC_for_traffic_control_during_incidents.pdf

Multistage Fuzzy Logic Controller for Expressway Traffic Control during Incidents

This paper presents part of a multi-stage fuzzy logic controller (MS-FLC) that is developed for traffic control in congestion management on expressways. The decision-making process of traffic control for expressway congestion management using the MS-FLC consists of three tasks: (1) evaluation of current traffic congestion; (2) prediction of traffic congestion tendency; and (3) recommendation of control strategies and control actions to alleviate the congestion. This paper presents the 3rd stage of the MS-FLC that develops a fuzzy-based decision-making procedure (FDMP) for management of recurring and non-recurring congestion. Using fuzzy rules, the FDMP evaluates the current and anticipated traffic data and incident information to recommend control strategies at the strategic level, and control actions at the operational level. Results from this research show that: (i) the FDMP offers a comprehensive procedure in deriving control strategies and actions; (ii) FDMP control actions are derived from a systematic decision-making logic where the design of control rules is consistently oriented toward achieving desirable control objectives; (iii) the FDMP targets a proper balance in congestion management between the mainline and the ramp using compromise rule design; (iv) the FDMP facilitates using various forms of available traffic and incident data on an extended expressway segment to derive at control actions, making the system-wide gains possible; and (v) the FDMP could be applied for management of both recurring and non-recurring congestion. 

Developing a fuzzy-based decision-making procedure for traffic control in expressway congestion management

The traffic carrying capacity of the road network is limited. When the limit is exceeded, traffic congestion will restrict the control and development of transportation systems. This paper aims to determine the connotation of traffic carrying capacity in some adjacent weaving sections (AWS) and put forward a novel calculation model for self-organizing carrying capacity. Firstly, the in-depth analysis found that each weaving section has a local-whole and interactive relationship with AWS. Secondly, the self-organizing critical state of AWS is analyzed by calculating the threshold from slow to congestion. Finally, the Renhe Interchange was used as an experimental object to study the carrying capacity under the self-organizing critical state of AWS. The VISSIM software and Microsoft Visual Studio 2020-C# software are employed to verify the model. The self-organizing critical occupancy rate before the optimization is 38.60%, and the carrying capacity is 7527 pcu/h. After optimization, the self-organizing critical occupancy rate is 48.70%, and the carrying capacity is 9497 pcu/h. The results show that the model can simulate the carrying capacity of self-organized criticality. It can also provide a basis for urban managers to maintain the stable development of the system and ensure the overall operation quality of the AWS. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/itr2.12261

Self‐organizing traffic control using carrying capacity modelling in adjacent weaving sections

On-ramp merging areas are congestion-prone segments of freeways. Depending on the aggressiveness of the driving population and the congestion level, the speed variance among travel lanes du...

Lane Group-Based Traffic Model for Assessing On-Ramp Traffic Impact

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Lane Group-Based Traffic Model for Assessing On-Ramp Traffic Impact