Vehicle Delay Research Articles

Decreased performance at unsignaled intersections affects the construction of the Solo-Yogya road with the least square method

The construction of the Solo-Yogyakarta toll road is part of the National Strategic Project. At the development stage, toll road infrastructure needs to assess the impact of traffic, considering many security and safety disturbances. Road performance evaluation is essential to overcome traffic problems during toll road operations in the future. The purpose of the study was to calculate traffic performance at the unsignaled intersection affecting the construction of the Solo-Yogya toll road. The locations studied were four Solo-Yogya toll road access intersections using primary data on the condition of existing non-toll roads. Carry out traffic surveys of the number of vehicles, travel time, and vehicle speed. The performance of the unsignaled intersection was calculated using Jica Strada's modeling with applicable toll road tariffs and traffic growth of 5.6% per year. The performance of the unsignaled intersection at the construction of the Solo-Yogya toll road in 2022 has an average Volume-Capacity Ratio (VCR) value of 0.61. In 2046, it has an average Volume-Capacity Ratio value of 0.99. At the intersection of Boyolali-Kartosuro-Banyudono and the intersection Kartosuro-Klaten-Ngaron, it is recommended to make an Interchange before 2032. The recommendation for making the Kartosuro and Boyolali Interchange is because in 2032 the Volume-Capacity Ratio is more than 0.8 to reduce vehicle delays.

Reasons for using public transport services in Cracow in the opinion of its users – survey results

Public transport services are an example of important category for the implementation of key activities by the local community. These services enable the movement of residents and guests visiting destinations and provide access to city elements of the settlement. The aim of the article is to identify the reasons for using public transport services in Cracow in the group of respondents. Selected characteristics of public transport are outlined on the basis of an analysis of the published literature. Particular attention was paid to the offer of Cracow’s public transport. The study presents the results of surveys conducted by an online diagnostic survey among users of the surveyed services. The aim of the research was to identify the reasons why the respondents use Cracow’s public transport services are: the lack of a car, a travel allowance and an attractive connection network turned out to be a group of key reasons for using the analyzed services. The results indicate the need to continuously improve the level of functioning of Cracow’s public transport by: information about vehicle delays, improving the operation of ticket vending machines, and by providing a regular number of conn.

Using microsimulation to investigate the optimal deployment of leading pedestrian intervals at signalized intersections

The provision of leading pedestrian intervals (LPI) has emerged in recent years to achieve safety equitability for pedestrians at signalized intersections. LPI is a way to provide the pedestrian walk interval a few seconds before starting the circular green indication to adjacent parallel traffic. Although the safety benefit of LPI is indisputable, there are fundamental questions that need to be addressed for the optimal deployment of this strategy. First, can significant safety benefits for pedestrians be achieved while maintaining a satisfactory operational level of service for vehicles? Second, what are the application circumstances most conducive to achieving the greatest safety benefits for pedestrians? Third, how can a jurisdiction effectively assess contemplated treatments to achieve optimal deployment? This exploratory paper addresses these three fundamental questions by reviewing relevant literature before presenting the research from the application of microsimulation to fifteen Toronto intersections where LPIs have been implemented. The microsimulation involved using a recently released module for accommodating LPI phasing in the PTV Vistro software. To directly address the first and second questions, vehicle-to-pedestrian conflicts and vehicle delay were estimated for ten scenarios that allowed for the provision of, and variability in the LPI interval, right turn volumes, right turn on red provision, pedestrian and vehicle volumes, and crossing width. The results suggest that significant safety benefits can be achieved for pedestrians while maintaining a satisfactory level of service for vehicles. They further suggest that potential LPI deployments need to be assessed on a case-by-case basis since the effects of LPI can be significantly impacted by the influencing factors investigated. Statistical models were developed to quantify the effects of LPI implementation on vehicle-to-pedestrian conflicts after controlling for pedestrian and turning vehicle volumes. The results of this exploratory investigation, though interesting and consistent with the literature and logical considerations, may not be generalizable in a strict sense. Nevertheless, the study does provide a blueprint for investigating the design, traffic, and operational factors that can influence the impact of LPI on pedestrian safety without detrimentally impacting vehicle level of service.

Signal timing at an isolated intersection under mixed traffic environment with self‐organizing connected and automated vehicles

AbstractThis study provides a signal timing model for isolated intersections under the mixed traffic environment consisting of connected and human‐driven vehicles (CHVs) and connected and automated vehicles (CAVs). Different from existing studies, CAVs are not controlled by traffic controllers and conduct trajectory planning themselves, which are called self‐organizing CAVs (SOCAVs). The specific trajectory planning strategies of SOCAVs are not accessible to traffic controllers either. The signal optimization problem is formulated as a mixed integer linear programming (MILP) model for total vehicle delay minimization. The states of SOCAVs and CHVs passing the stop bar are predicted without prior information of the trajectory planning strategies of SOCAVs. SOCAVs can lead approaching platoons to pass the intersection effectively, and such “leading effects” of SOCAVs are utilized. Phase sequence and duration are optimized with the “structure‐free” phasing scheme. A parallel particle swarm optimization algorithm with a grouping strategy is designed to solve the optimization model at a reduced scale for computational efficiency. Numerical studies validate that (1) the proposed algorithm significantly outperforms the benchmark method, which directly solves the proposed MILP model using the solver Gurobi 9.0, under medium and high traffic demand; and (2) the proposed model significantly outperforms fixed‐time and vehicle‐actuated signal control in terms of vehicle delay and throughput. Sensitivity analysis shows that the SOCAV penetration rate of 30% is sufficient to guarantee satisfactory performance of the proposed signal timing model.

Assessing the Impact of Increasing Tractor-Trailer Speed Limit on the Safety and Mobility of Three-Lane Highways in Egypt

Currently, the speed limit for tractor-trailers (TTs) on Egyptian highways is 30 km/h lower than the speed limit for passenger cars (PCs). The main purpose of this article is therefore to evaluate the effects of an increase in TT speed limit on both the traffic safety and mobility of Egyptian three-lane highways, with speed limits of 90 and 100 km/h for PCs. A SUMO simulation model was used in this analysis. Average vehicle delay was used as a measure of mobility, while the simulated conflict ratio based on time to collision (TTC) and deceleration rate required to avoid crash (DRAC) surrogate safety measures were used as performance indicators of safety. All simulated vehicles were equipped with surrogate safety measurement (SSM) devices to extract traffic conflicts, with TTC ≤ 2.50 representing less sever conflicts; TTC ≤ 1.50 s and DRAC ≥ 3.35 m/s2 representing potential conflicts; and TTC ≤ 0.50 s and DRAC ≥ 6.0 m/s2 representing severe conflicts. The results show that increasing the speed limit for TTs would significantly reduce the average delay by approximately 27–76% for traffic volumes 500–2000 vehicle/hr/lane, with TTs comprising 2.5–15% of total traffic volume. Furthermore, in most cases, when comparing the situation after the speed limit increase for TTs with the base case (i.e., TTs with a speed limit of 60 km/h and 70 km/h for highways, and a speed limit of 90 km/h and 100 km/h for PCs), the results show no statistically significant change in the conflict ratio more than 1.0, suggesting that there is no safety change. Furthermore, due to the increase in speed limit for TTs, in some cases and for different thresholds, there is a statistically significant reduction in the conflict ratio, indicating improvements in terms of safety.

Federated Deep Reinforcement Learning Based Task Offloading with Power Control in Vehicular Edge Computing.

Vehicular edge computing (VEC) is a promising technology for supporting computation-intensive vehicular applications with low latency at the network edges. Vehicles offload their tasks to VEC servers (VECSs) to improve the quality of service (QoS) of the applications. However, the high density of vehicles and VECSs and the mobility of vehicles increase channel interference and deteriorate the channel condition, resulting in increased power consumption and latency. Therefore, we proposed a task offloading method with the power control considering dynamic channel interference and conditions in a vehicular environment. The objective is to maximize the throughput of a VEC system under the power constraints of a vehicle. We leverage deep reinforcement learning (DRL) to achieve superior performance in complex environments and high-dimensional inputs. However, most conventional methods adopted the multi-agent DRL approach that makes decisions using only local information, which can result in poor performance, while single-agent DRL approaches require excessive data exchanges because data needs to be concentrated in an agent. To address these challenges, we adopt a federated deep reinforcement learning (FL) method that combines centralized and distributed approaches to the deep deterministic policy gradient (DDPG) framework. The experimental results demonstrated the effectiveness and performance of the proposed method in terms of the throughput and queueing delay of vehicles in dynamic vehicular networks.

ANALISIS KINERJA LALU LINTAS JL. RAYA MR. MOCH. ICHSAN, KOTA SEMARANG (DEPAN PINTU KELUAR MASUK RS. PERMATA MEDIKA)

Semarang is one of the metropolitan. Traffic on Jl. Raya Mr Moch. Ichsan in front of the Permata Medika hospital, which is fairly crowded, is due to the influence of the hospital's activities. Many activities are seen at the entrance and exit of the hospital, including crossing. The purpose of this study was to analyze traffic performance, determine the factors that hinder traffic flow, determine the volume, travel time, and vehicle delay time due to u-turns at the median opening in front of the hospital entrance. Medical Gems. Traffic volume data collection was carried out for 3 days, namely weekdays, short days. and holidays using the 2014 PKJI method (referring to the reference to the 1997 MKJI basic rules). The results of the observations obtained are that the total current is 6201 cur/hour with the side resistance weight being at a moderate level. By following the 2014 PKJI guidelines, we get the adjustment values ​​for Free Flow Speed ​​(VB) = 61 km/hour and Capacity (C) = 7128 skr/hour. With a value of DJ = 0.869 cur/hour, it is at LOS level E which according to PKJI 2014 (referring to the reference to the 1997 MKJI basic rules) the level of road service is above the average for the maximum service level, namely levels A to D with a degree of saturation value of 0.00 - 0.84. Strict action from the relevant officers is needed by controlling existing traffic signs such as parking is prohibited in order to maximize the function of the existing road capacity.

Analysis of Motorized Vehicles Performance at Signalized Intersections Based on Micro Simulation

Based on microsimulation, this study analyses the performance and behavior of motorized vehicles at the signalized intersection between the railway line and Ahmad Yani road-Jemursari road. For the survey of the volume of motorized vehicles, this intersection is divided into four intersections with eight directions of traffic movement and one direction of combined traffic movement. Microsimulation was carried out using Vissim software with nine parameters calibrated according to actual conditions during the peak hour observation period. The maximum queue length and motor vehicle delays for the peak hour observation period occur at intersections one in the north and two in the east. Performance of the intersection of Jl. Ahmad Yani-Jl. Jemursari is included in category F. The indicator F are traffic jams, low vehicle speeds, long queues, and vehicle delays. The following study of this paper is to make a geometric model of the intersection into a Fly Over model.

Coordinated Control Model for Oversaturated Arterial Intersections

In order to improve the traffic efficiency of oversaturated arterial traffic, a two-way signal coordinated control method is proposed. Taking traffic speeds, turning flow proportions and the number of lanes into consideration, a traffic flow model for oversaturated arterial is established on the basis of LWR theory, and vehicle trajectory data of arterials is used to verify this model. Then a coordinated control model for two-way oversaturated arterials is introduced, which is composed of a throughput maximization model and a delay minimization model. For the former one, the cycle length and green time at the arterial intersections are optimized to improve the arterial capacity, while the latter is built to realize the signal coordination of the consecutive intersections by optimizing offsets. A multi-objective algorithm is proposed to solve the coordinated control model. First, the throughput maximization model is solved by the standard algorithm of mixed-integer linear programming. Second, the feasible solutions of the signal orders are obtained by a phase pattern searching algorithm. Third, the delay minimization model is solved by a standard algorithm of quadratic programming model. Finally, the proposed model is used to coordinate and optimize the traffic signals on East Zhongshan Road containing 10 intersections in Nanjing, China. A trajectory-based simulation model is put forward to simulate the arterial traffic, demonstrating that the proposed coordinated control model can avoid the spillover and queue retention. After optimization, the capacity increases by 12.6% while the average vehicle delay for through vehicles drops by 23.6% compared to Synchro’s coordinated control.

QoS-Aware Joint Task Scheduling and Resource Allocation in Vehicular Edge Computing

Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU), aiming to thereby reduce the processing delay and resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to achieve both low latency and low energy consumption. Although existing works have made great contributions, they rarely consider the coordination of multiple RSUs and the individual Quality of Service (QoS) requirements of different applications, resulting in suboptimal offloading policies. In this paper we present FEVEC, a Fast and Energy-efficient VEC framework, with the objective of realizing an optimal offloading strategy that minimizes both delay and energy consumption. FEVEC coordinates multiple RSUs and considers the application-specific QoS requirements. We formalize the computation offloading problem as a multi-objective optimization problem by jointly optimizing offloading decisions and resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem and NP-hard. We propose MOV, a Multi-Objective computing offloading method for VEC. First, vehicle prejudgment is proposed to meet the requirements of different applications by considering the maximum tolerance delay related to the current vehicle speed. Second, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to obtain the Pareto-optimal solutions with low complexity. Finally, the optimal offloading strategy is selected for QoS maximization. Extensive evaluation results based on real and simulated vehicle trajectories verify that the average QoS value of MOV is improved by 20% compared with the state-of-the-art VEC mechanism.

Proposing a kinematic wave‐based adaptive transit signal priority control using genetic algorithm

AbstractThis paper proposes a kinematic wave‐based adaptive transit signal priority control (KATC) aiming to minimize average passenger delay at intersection level. The passenger delay minimization problem is formulated as a mixed‐integer non‐linear program (MINLP) with two decision variables of green time and phase sequence. The adoption of the phase sequence in the optimization and not involving the common simplifying assumptions in the delay models are the main contributions of the current study. General traffic and public transportation (PT) vehicle delay are estimated using kinematic wave theory. Genetic algorithm is utilized to solve the MINLP problem at 1‐cycle intervals and for a decision horizon of 3 consecutive cycles. The performance of KATC is evaluated against SYNCHRO and the KATC model without phase sequence optimization, using VISSIM. The results of the experiments indicate superior performance of KATC over the two other models in terms of both average passenger delay and PT passenger delay, especially at low congestion levels. Furthermore, increasing PT passenger occupancy can effectively contribute to higher passenger delay improvements. The adverse impacts on passenger vehicles are restricted to a 3.4% and 2.7% increase in general traffic delay compared to SYNCHRO and the KATC model without phase sequence optimization, respectively.

Efficiency Assessment of New Signal Timing in Saudi Arabia Implementing Flashing Green Interval Complimented with Law Enforcement Cameras

Traffic congestion at intersection is one of the significant socioeconomic concerns worldwide. To tackle this challenge, researchers and practitioners are researching and executing different plans to control and manage long queues and delays. The general department of traffic in Saudi Arabia has implemented a new signal timing pattern in a number of signalized intersections that were designed with an additional flashing green phase complemented with law enforcement cameras (SAHER) to improve the capacity and safety of signalized intersections. This research aims to evaluate the impact of flashing green intervals on driver behavior and traffic efficiency of five signalized urban intersections equipped with SAHER in the Al-Qassim region, Saudi Arabia. Analyses for the current situation (base scenario) and proposed scenarios (without SAHER) are performed and validated using the microsimulation model (VISSIM) with field collected data at the selected intersections. The results showed that, despite fewer improvements in vehicle delays, the intersections without SAHER and flashing green intervals yield shorter queue lengths than the intersections with SAHER and flashing green intervals. Further, it was also revealed that drivers tend to stop early and start late in the case of SAHER due to fear of red light fines, thus not utilizing the full green split and yellow time. Analysis for the average vehicle delay and queue lengths is also conducted to assess the efficacy of implemented green light flashing with SAHER on driver behavior and operational efficiency of the selected intersections.

Calculation of the delays of autonomous vehicles at signalized intersections using the Webster method

Traffic lights as one of the key components at intersections, have an important role in the control of traffic flow. Delays occurring at signalized intersections significantly affect the travel time. Hence, coordinating intersections is one of the most effective ways to achieve a steady flow of traffic. By means of illuminated and signalcoordinated intersections, the delay time, queue length, and travel time of vehicles moving on arterial roads may be significantly reduced. Autonomous vehicles developed within the scope of todays technology have now become a part of transportation systems. This paper evaluates the effectiveness of autonomous vehicle systems to minimize delays at intersections.

Assessment Performance at Al- Thawra Signalized Intersection in Babil City

Hilla cities center of province Babil is one of the most important cities in Iraq 100 km (62 mi) south of Baghdad .its relate the Governorate Baghdad with Governorates south Al-Thawra signalized intersection is one of the most important intersections in AL-Hilla city Being a major crossing point to the governorates of holy Karbala and Baghdad.The excessive traffic volumes, during the peak periods (at morning and evening), of vehicles that entering the intersection increase traffic density, reduce travel speed, increase travel time, and increase the delay values This paper aims to assess the traffic performance of Al-thawra signalized intersection in Babil city that is consistent with the existing conditions and intersection's geometric properties. For all approaches, video recording has been used to collect traffic volume data. These data are taken out of videos from Babil police cameras (department of communications and information systems)..The intersection was evaluated and analyzed using the SYNCHRO 10.0 software, and the best option was selected. The outcome of the evaluation process indicated that the intersection is operated at the level of service (LOS F) and with a control delay of 162.5 seconds.by recommending a variety of strategies, ranging from signal optimization to geometric enhancements. The suggestion of widening the pavement in the north-south direction was found to be the best solution. resulting in a decrease in vehicle delays from 162.2 seconds to 95.4 seconds and the level of service remains constant (LOS F) considered an acceptable and cost-effective solution to the intersection's problems.

Dynamic Scheduling and Path Planning of Automated Guided Vehicles in Automatic Container Terminal

The uninterrupted operation of the quay crane (QC) ensures that the large container ship can depart port within lay-time, which effectively reduces the handling cost for the container terminal and ship owners. The QC waiting caused by automated guided vehicles (AGVs) delay in the uncertain environment can be alleviated by dynamic scheduling optimization. A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems, in which the scheduling scheme determines the starting and ending nodes of paths, and the choice of paths between nodes affects the scheduling of subse-quentAGVs. This work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime. A dynamic optimization algorithm, including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the Q-Learning algorithm, is designed to solve the optimal AGV scheduling and path schemes. A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between AGVs. Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.

Human‐centric multimodal deep (HMD) traffic signal control

AbstractReinforcement learning (RL)‐based models have been widely studied for traffic signal control with objectives, such as minimizing vehicle delay and queue length, maximizing vehicle throughput, and improving road safety, through tailored reward designs. Despite the advancements in RL‐based signal control models for car traffic, limited research focused on multimodal traffic (e.g. car, bus, pedestrian). The simplified environment of unimodal traffic restrains these models from applications in real‐world cases. In this paper, the authors propose an RL‐based human‐centric multimodal deep (HMD) traffic signal control method to coordinate multimodal traffic at an intersection, with the objective of minimizing the waiting time per capita for multiple traffic modes by taking consideration of the mode capacity to ensure social equity. The method is validated in the simulation of urban mobility (SUMO) simulation environment using real traffic data. The results show the superior performance of HMD over vehicle‐centric (VC) RL‐based methods and traditional signal control schemes. HMD reduces the waiting time per capita by up to 19.2% and 6.3% compared with the fixed timing and VC RL‐based methods. In addition, the experiment results show that the HMD method assigns a higher priority to public transport over low‐occupancy travel modes in passing through intersections compared with unimodal traffic signal control strategies.

Changeable shared bus lane scheme for intersections based on lane signals

Abstract To address situations in which the bus lanes at intersections are idle for long periods, this paper proposes a changeable shared bus lane scheme for intersections based on lane signals. This scheme set up two GPS monitoring points for buses in the bus lane of the intersection to monitor the bus approaching the intersection. The lane signals linked to the monitoring points provide car drivers with a signal of whether they can enter the bus lane. This paper establishes an optimization model to maximize the number of vehicles entering the bus lane and uses numerical example simulations to discuss in detail the optimal monitoring point locations in different situations. Without affecting the bus, we obtain the optimal monitoring point setting schemes for several common situations and compare the total delay for ordinary vehicles in the bus lane of the lane-based signal scheme and a baseline case (without the lane-based signal). The results show that this scheme can effectively reduce vehicle delays at intersections. Finally, we discuss the limitations of this scheme and directions for future research.

Real-Time Adaptive Traffic Signal Control in a Connected and Automated Vehicle Environment: Optimisation of Signal Planning with Reinforcement Learning under Vehicle Speed Guidance.

Adaptive traffic signal control (ATSC) is an effective method to reduce traffic congestion in modern urban areas. Many studies adopted various approaches to adjust traffic signal plans according to real-time traffic in response to demand fluctuations to improve urban network performance (e.g., minimise delay). Recently, learning-based methods such as reinforcement learning (RL) have achieved promising results in signal plan optimisation. However, adopting these self-learning techniques in future traffic environments in the presence of connected and automated vehicles (CAVs) remains largely an open challenge. This study develops a real-time RL-based adaptive traffic signal control that optimises a signal plan to minimise the total queue length while allowing the CAVs to adjust their speed based on a fixed timing strategy to decrease total stop delays. The highlight of this work is combining a speed guidance system with a reinforcement learning-based traffic signal control. Two different performance measures are implemented to minimise total queue length and total stop delays. Results indicate that the proposed method outperforms a fixed timing plan (with optimal speed advisory in a CAV environment) and traditional actuated control, in terms of average stop delay of vehicle and queue length, particularly under saturated and oversaturated conditions.

A Critical Review of Grade-Separated Pedestrian Crossing Facilities

Vehicle–pedestrian crashes are one of the common road accident types. Pedestrian crossing warrants are guidelines that recommend a particular type of pedestrian crossing facility to be provided at a crossing location. Various countries around the world have their crossing warrant guidelines, but most of them are focused toward at-grade facilities. Even with existing warrant criteria, the number of pedestrian–vehicle crashes is alarming. This indicates that the warrant criteria and their threshold values may need reexamination. Several of the existing warrant guidelines do not provide the required clarity for the provision of grade-separated pedestrian crossing facilities (GSPCFs). GSPCFs include overpasses (or foot overbridges), underpasses (or subways), and partial underpasses (or hump subways). The existing warrant guidelines consider GSPCFs as a single unit and do not identify which type of GSPCF should be provided at a crossing location. Appropriate GSPCFs not only minimize the accident rate but also eliminate delays for both vehicles and pedestrians. This paper conducts an in-depth analysis of the existing literature on urban midblock GSPCF warrants around the world. The discussion highlights the limitations associated with existing warrant guidelines and the need to formulate improved GSPCF warrants.

Modeling the Impact of COVID-19 on Traffic Operation, Emission and Queue Length: a Case Study

The global COVID-19 outbreak has caused a drastic change in human travel behavior. Most of the measures that have been taken by governments and local authorities involve behavioral changes from citizens to reduce their social contact to a minimum in order to control the spread of the virus. Thus, these actions affect individual activity patterns and transport systems differently. This paper quantifies the impact of COVID-19 on intersection operation performance using data from the control management room at Greater Amman Municipality (GAM) in Jordan. In this paper, the operational performance of one of the heaviest signalized intersections in Amman has been modeled under COVID-19 prevention measures. The intersection's performance has been evaluated under three scenarios, namely, before CVOID-19 hits, using an odd/even license plate strategy with activities closure, and using a 70% capacity restriction. In the Analytical Hierarchy Process (AHP), multi-criteria have been used. Three criteria groups (efficiency, capacity, and environmental) have been developed, which have contained eight sub-criteria. The weights of the criteria groups have been varied based on practitioners' and academic perspectives. The application of the developed methodology has enabled the selection of the most suitable travel restriction decision that has helped improve the transportation system's operational status. The evaluation sub-criteria in the study are vehicle delay, queue length, stopped delay, stops, travel time, vehicle safety, CO emission, and fuel consumption. Results have indicated that, by comparing the intersection operation performance before and after COVID-19, there is a significant improvement in intersection performance due to the travel restrictions during the pandemic effect. Moreover, AHP has indicated that the odd/even system is the best strategy to improve intersection performance. The results have indicated that the odd/even system has the best operational performance compared to the other two travel restrictions.