Arterial Intersections Research Articles

Optimising Layout of a Left-Turn Bypass Intersection under Mixed Traffic Flow using Simulation: A Case Study in Pulau Pinang, Malaysia

Various unconventional arterial intersection designs (UAIDs) have been proposed as novel approaches to alleviate congestion at signalised intersections. However, little research has focused on the UAIDs under mixed traffic flow. In Malaysia, a type of unconventional arterial intersection, i.e., the left-turn bypass intersection, is very common. Hence, an alternative two-lane left-turn bypass intersection layout is proposed and compared with two other layouts under mixed traffic flow. An existing intersection in Pulau Pinang, Malaysia is selected as the location of our case study. To compare the three layouts, the state-of-the-art traffic micro-simulation software SUMO is used to model, analyse and estimate operational performances of the studied intersection layouts. To make simulation results more reliable, the simulation model is validated using the GEH empirical test. The performance indicators delay, travel time, speed, and number of stops are used to evaluate the three layouts for low, medium and high traffic volumes. The simulation results show that the left-turn bypass intersection outperforms the intersection without any bypasses, and the proposed intersection performance is better than the two other considered intersections. More specifically, the average delay of the proposed intersection is lower by 2.54s and 4.4s compared to the two other considered intersections in high traffic volume, respectively. Thus, this proposed intersection could be used in urban cities with traffic demand and congestion issues similar to those in this study.

The Feasibility and Operational Performance of Implementation Median U-turn Intersections: A CRITIC Method

Currently, a growing number of cities are adopting the Median-U-Turn (MUT) intersection design to enhance road capacity and traffic efficiency. The critical question in selecting the right intersection design is how significantly the implementation of MUT can enhance intersection performance, focusing on three key aspects: intersection efficiency, capacity, and the environmental impact of the design. To address this question, an evaluation of operational performance under various prevailing conditions (roadway and control) was conducted using VISSIM, a microscopic simulation platform. This evaluation involved five scenarios: conventional intersections (with increased cycle length, grade separation with a signalized at-grade intersection, grade separation with a roundabout), MUT, and signalized crossover MUT at a dense urban arterial intersection in Amman, Jordan's capital. The performance was compared using several metrics: average control delay, number of stops, average travel speed and time, average stopped delay, Carbon Monoxide (CO) emissions, fuel consumption, and vehicle safety. The Criteria Importance Through Intercriteria Correlation (CRITIC) technique was subsequently used to select the optimal design. The findings indicate that the existing intersection configuration is the least effective, while the MUT with signals at the crossing U-turn points is the most efficient solution.

Multi-objective arterial coordination control method based on induction control and vehicle speed guidance

The fixed green wave speed and staged statistical flow used in arterial signal coordination are not adaptable to the fluctuations in vehicle travel speed and traffic flow on roads, resulting in a mismatch between the signal scheme and the optimal green wave speed and traffic flow demand. This discrepancy negatively impacts the efficiency of intersection traffic. In traditional signal control systems, the cycle and green light timing are typically set independently. However, such a setting method poses problems in practical operation. In this paper, we combine vehicle arrival and vehicle location information, and consider the interaction of speed guidance and dynamic signal optimization to construct a model. This study is developed along the following steps: in the vehicle-road coordination environment, based on the MAXBAND model, a global coordination scheme is obtained, incorporating the speed guidance method; then, based on the vehicle saturation of the inlet lane of the arterial intersection, a multi-objective optimization model for arterial signal coordination under vehicle speed guidance is established based on global coordination with the maximum green wave bandwidth and the minimum delay of arterial vehicles, the minimum number of arterial stops and the minimum delay in the minor direction road as the optimization objectives. Based on global coordination, adopting an integrated control mechanism of cycle and green light timing allows for dynamic adjustments according to real-time traffic conditions. The improved multi-objective particle swarm algorithm is chosen to solve the model, and the simulation environment is built based on the COM interface of VISSIM software and C# platform. Three adjacent intersections of Ganjiang Middle Road in Nanchang are selected as case studies, and the methods in this paper are compared with the current timing scheme, the MAXBAND method and the optimization scheme under speed guidance only, respectively. The results show that the model proposed in this paper achieves significant optimization effects on the indicators of arterial delay, arterial stopping times and the delay of minor roads.

OAS Deep Q-Learning-Based Fast and Smooth Control Method for Traffic Signal Transition in Urban Arterial Tidal Lanes.

To address traffic flow fluctuations caused by changes in traffic signal control schemes on tidal lanes and maintain smooth traffic operations, this paper proposes a method for controlling traffic signal transitions on tidal lanes. Firstly, the proposed method includes designing an intersection overlap phase scheme based on the traffic flow conflict matrix in the tidal lane scenario and a fast and smooth transition method for key intersections based on the flow ratio. The aim of the control is to equalize average queue lengths and minimize average vehicle delays for different flow directions at the intersection. This study also analyses various tidal lane scenarios based on the different opening states of the tidal lanes at related intersections. The transitions of phase offsets are emphasized after a comprehensive analysis of transition time and smoothing characteristics. In addition, this paper proposes a coordinated method for tidal lanes to optimize the phase offset at arterial intersections for smooth and rapid transitions. The method uses Deep Q-Learning, a reinforcement learning algorithm for optimal action selection (OSA), to develop an adaptive traffic signal transition control and enhance its efficiency. Finally, a simulation experiment using a traffic control interface is presented to validate the proposed approach. This study shows that this method leads to smoother and faster traffic signal transitions across different tidal lane scenarios compared to the conventional method. Implementing this solution can benefit intersection groups by reducing traffic delays, improving traffic efficiency, and decreasing air pollution caused by congestion.

Research on coordinated control optimization of adjacent intersections based on numerical solution

In order to improve the efficiency of traffic flow and reduce the delay of arterial intersections, a coordinated signal control method based on numerical solution is proposed. Firstly, considering the factors such as the shortest green time and saturation of a single intersection, the intersection delay model is established. Secondly, the traffic capacity of the trunk line is improved by optimizing the public cycle and the green ratio of each intersection. Finally, an optimization model with minimum delay as the optimization objective is constructed, and the signal coordination of arterial intersections is realized by taking Yancheng City as an example. The results show that the coordinated control model of adjacent intersections based on the numerical method can reduce the average vehicle delay of two-way vehicles, achieve the purpose of green wave coordinated control of road sections, and improve the road traffic capacity.

Coordination Optimization of Real-Time Signal Priority of Self-Driving Buses at Arterial Intersections Considering Private Vehicles

Transit Signal Priority (TSP) is a system designed to grant right-of-way to buses, yet it can lead to delays for private vehicles. With the rapid advancement of network technology, self-driving buses have the capability to efficiently acquire road information and optimize the coordination between vehicle arrival and signal timing. However, the complexity of arterial intersections poses challenges for conventional algorithms and models in adapting to real-time signal priority. In this paper, a novel real-time signal-priority optimization method is proposed for self-driving buses based on the CACC model and the powerful deep Q-network (DQN) algorithm. The proposed method leverages the DQN algorithm to facilitate rapid data collection, analysis, and feedback in self-driving scenarios. Based on the arrival states of both the bus and private vehicles, appropriate actions are chosen to adjust the current-phase green time or switch to the next phase while calculating the duration of the green light. In order to optimize traffic balance, the reward function incorporates an equalization reward term. Through simulation analysis using the SUMO framework with self-driving buses in Zhengzhou, the results demonstrate that the DQN-controlled self-driving TSP optimization method reduces intersection delay by 27.77% and 30.55% compared to scenarios without TSP and with traditional active transit signal priority (ATSP), respectively. Furthermore, the queue length is reduced by 33.41% and 38.21% compared to scenarios without TSP and with traditional ATSP, respectively. These findings highlight the superior control effectiveness of the proposed method, particularly during peak hours and in high-traffic volume scenarios.

Information dissemination dynamics through Vehicle-to-Vehicle communication built upon traffic flow dynamics over roadway networks

This research is dedicated to developing a discrete mathematical simulation framework to track information dissemination dynamics via Vehicle-to-Vehicle (V2V) communication factoring traffic flow dynamics, traffic intersection operation settings, and traffic intersection geometrical design over a road traffic network. Specifically, we develop information network flow models (INFMs), including IFNM-a and IFNM-r, respectively for tracking information wavefront spreading dynamics at arterial intersections and at highway-ramp intersections. Next, by integrating IFNMs with the information and traffic coupled cell transmission model (IT-CTM) model developed by Du et al. [16] for capturing the information front propagation dynamics on a road segment, we establish a discrete mathematical simulation framework (IFNM-CTM) to track the information front spreading dynamics over a road network at discrete time stamps. Furthermore, by combining the IFNM-CTM framework and the deep search algorithms, this study tracks the information coverage dynamics and investigates its correlation to traffic congestion evolution over a traffic network at discrete time stamps. Our experiments built upon Sioux Falls city network indicate that the IFNM-CTM is able to track the information front spreading, including location and coverage, accurately with the mean absolute error (MAE) less than 6% and 5%, respectively. More importantly, our studies found a strong correlation existing between information front spreading dynamics and traffic congestion evolution over the network. Specifically, a mild congestion condition (i.e., LOS C and D) provides the best traffic condition to sustain information spreading as compared to sparse (i.e., LOS A or B) and heavily congested (i.e., LOS E or F) traffic conditions since neither of them can sustain stable and constant wireless communication due to the limited transmission range of DSRC and interference issues.

Cost–Benefit Analysis of Unconventional Arterial Intersection Designs: Cairo as a Case Study

Due to their innovative treatment, Unconventional Arterial Intersection Designs (UAIDs) have been developed to alleviate congestion at conventional signalized intersections, in an effort towards the sustainable development of crowded capitals. A methodological framework for economic assessment, however, has not been investigated properly for such designs, particularly under mixed traffic environments. This article aims to outline a methodological framework that can be followed for the socio-economic assessment of such designs. A cost–benefit analysis approach was developed to figure out the different determinants of costs and benefits of an overpass interchange (as a widespread treatment) and two selected UAIDs (as alternative measures). The two studied UAID schemes in this article are Continuous Flow Intersection (CFI) and Restricted Crossing U-Turn (RCUT). Seeking credible results, a set of three signalized intersections in downtown Cairo, Egypt was selected as a proof-of-concept for the developed method. PTV-VISSIM, a simulation-based platform, was utilized to estimate the benefits gained by road users. Our research objectives were to identify, evaluate, and compare the economic feasibility of the different alternatives. Compared to the overpass intersection, we found that the CFI and RCUT designs ensure higher economic efficiency, while mitigating congestion at conventional signalized intersections under heterogeneous traffic conditions.

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.

Comprehensive Evaluation of Operational Efficiency of Intersections in Arterial Considering Pedestrians Yield Rule

Yield to pedestrians has become a new trend of civilized transportation in the metropolis. In order to evaluate the influence of yield behavior on the comprehensive operation efficiency of signalized intersections in arterial, the efficiency of the subject who gives way and the subject who is given up in the process of yielding to pedestrians was considered in this study, and the comprehensive operation evaluation of intersection in arterial was given. First, based on the rule of pedestrians yield, the concept of a safe headway gap of pedestrians was introduced in the process of conflict analysis of traffic flows at the intersection, and 3 situations were discussed, which are no yield, yield to 1 flow and 2 flows, to calculate the departure rate of traffic flow at the intersection. Furthermore, models of 3 evaluation indices were established, which are number of people passing per unit time, average delay per people, and average yield number per people at intersection. Moreover, the entropy weight method was taken to decide the weight of these 3 indices, and to calculate the comprehensive efficiency evaluation of intersection operation, with the standardized matrix. Finally, case study work was carried out to evaluate the comprehensive efficiency of 4 types of intersections in arterial considering pedestrians yield rule, which is the intersection between arterial and arterial (IAA), intersection between arterial and subarterial (IAS), intersection with one-time (IAA-1, IAS-1), and two-time (IAA-2, IAS-2) crossing of pedestrians. The relevant results show that the impact of an increase in the number of pedestrians on the combined efficiency of arterial intersections can vary dramatically in different scenarios. Therefore, in the implementation process of “yielding to pedestrians,” the flow fluctuation characteristics and channelization of each intersection should be taken into account, and the corresponding phase changes should be based on pedestrian and vehicle volumes to improve the efficiency of all parts, such as pedestrians and drivers.

Subcycle Waveform Modeling of Traffic Intersections Using Recurrent Attention Networks

Traffic flow dynamics in the vicinity of urban arterial intersections is a complex and nonlinear phenomenon, influenced by factors such as signal timing plan, road geometry, driver behaviors, etc. Predicting such flow dynamics is an important task for urban traffic signal control and planning. Current methods use microscopic simulation for studying the impact of a large number of signal timing plans at each of the intersections. A major drawback of microscopic simulation is that they are they are based on source destination traffic generation models and cannot incorporate the high resolution loop detector data such as that are provided by automated traffic signal performance measures (ATSPM) based systems. The arrival (or departure) information of each vehicle on a detector can be thought of as a time series waveform. Given the high granularity of ATSPM data, this waveform can be used to several interesting analyses. The waveforms can be used to derive information on platoon dispersion as vehicles progress across the corridor. Also, these waveforms can be modelled to understand how the vehicles progress across the corridor for a variety of signal timing plans. In this paper, we show that deep neural networks based machine learning systems can be used to effectively leverage the waveforms collected at multiple sensors (stopbar and advanced) on the intersection to model the traffic dynamics both at an intersection and across intersections. We show that modelling of these waveforms can be useful to understand traffic flow dynamics under different signal timing plans and can be potentially integrated into signal timing optimization software. Further, these methods are three to four orders of magnitudes faster than using microscopic simulations.

Operational and Safety Performance Evaluation of Parallel Flow Intersection

Unconventional arterial intersection designs have recently gained popularity as an alternative solution to alleviate congestion and mitigate safety problems of at-grade signalized intersections. This study investigates the operational and safety performance of the parallel flow intersection (PFI) under different balanced and unbalanced traffic volume conditions in comparison with a conventional counterpart using VISSIM microsimulation and Surrogate Safety Assessment Model software. The operational performance of the two intersections was based on the overall intersection capacity and the average vehicle delay. The safety performance, on the other hand, was based on the number and severity of simulated conflicts. The results showed that the PFI had a higher capacity compared with the conventional intersection under balanced volume conditions. Under unbalanced volume levels, the PFI outperformed the conventional intersection especially with an increase of left-turn volumes. Moreover, the PFI enhanced the safety conditions relative to the conventional intersection by eliminating all the crossing conflicts under all balanced and unbalanced volume levels and reducing the total number and severity of conflicts at all volume conditions. The study provides guidelines for the optimal spacing distance between the main and secondary intersections for the implementation of the PFI based on its safety and operational impacts on the intersection.

Unconventional Intersection Designs for Improving Traffic Operation Along Arterial Roads

The main objective of this paper is to evaluate the effect of implementing four Unconventional Arterial Intersection Designs (UAIDs) including median U-Turn, Superstreet, Jughandle and Single Quadrant Intersection on a major arterial road using SYNCHRO microscopic simulation software. For this purpose, Wadi Saqra Signalized Intersection on Shaker Bin Zaid major arterial road in Amman, Jordan was selected. The simulation results showed that only the Jughandle improved the intersection Level of Service (LOS) slightly, F–E. Nevertheless, the intersection delay was significantly reduced by 64.81%, 76.6%, 91.28% and 75.60% on the proposed Median U-Turn, Superstreet, Jughandle and Single Quadrant unconventional intersection design, respectively. This indicated that these UAIDs don't perform well under heavy traffic volumes. Also, since the Jughandle was the only UAID which improved the LOS on the main intersection, the operational performance of Prince Shaker Bin Zaid arterial after implementing the Jughandle at the main intersection was evaluated including the main intersection: Wadi Saqra intersection, one prior to the main intersection and one after the main intersection. It was found that the use of the Jughandle increased the average travel speed by 35% and decreased the average stopped delay by 28.68% on the arterial road. Also, this paper evaluated the current transportation system and road user's attitude towards UAIDs' implementation through a questionnaire survey. The results indicated high acceptance of UAIDs. Finally, the construction cost for each UAID type was estimated. It was found that the Jughandle had the highest construction cost due to its high acquisition cost.

Implementation of a Double Continuous Flow Intersection in Riyadh

The continuous growth of population in the capital, coupled with increased auto ownership and dependence has worsened traffic conditions on Riyadh's road network. Conventional methods to address this increased demand could be costly and insufficient. There has been greater interest in using alternative measures to improve the performance and safety characteristics on main corridors, particularly those that arrive at signalized intersections. Heavy left turning traffic at these intersections is one of the main causes for delays. Previous research has investigated several types of alternative designs termed "unconventional" arterial intersection designs that could minimize the effect of left turning traffic. This paper provides decision makers with an objective assessment on the efficiency of implementing an unconventional intersection design, the Double Continuous Flow Intersection (DCFI) configuration, to improve the operational and safety characteristics of an existing major signalized arterial intersection in Saudi Arabia. In this study, the Kingdom Hospital Intersection in Riyadh was selected, as it is one of the most congested intersections in Riyadh. Using the collected traffic data, the micro-simulation program VISSIM was used to analyze and compare the efficiency of both configurations. When compared to the existing conventional signalized intersection design, it was found that the proposed Double Continuous Flow Intersection (DCFI) unconventional intersection design decreased the average delay per vehicle by 99 seconds. The proposed Double Continuous Flow Intersection configuration also improved the Level of Service at the intersection from level F (152 sec/veh average delay) to level D (53 sec/veh average delay).

The Impact of Pedestrian and Nonmotorized Vehicle Violations on Vehicle Emissions at Signalized Intersections in the Real World: A Case Study in Beijing

Emission around intersections has become an issue in the urban traffic network. This paper aims to investigate the impact of pedestrian and nonmotorized vehicle violations on emissions at mixed-traffic flow intersection based on the volumes of vehicles, nonmotor vehicles, and pedestrians. Also, it focuses on the arterial and collector intersections with high vehicle volume and limited space. Running red light and crossing intersection diagonally are two critical violations, accounting for 91.75% of effective violations (interference with vehicles’ operation). In this context, a violation blocking model is developed to estimate the blocking probability for each vehicle based on the volumes of pedestrians and nonmotor vehicles. The model includes two scenarios. (1) Through phase: the violation blocking model of running red light is developed based on the survival curve (the relationship between waiting time and running red light probability). (2) Left-turn phase: the violation blocking model at this phase includes two parts: (i) crossing the intersection diagonally model is developed for the first vehicle and (ii) running red light model is developed for subsequent vehicles. The existing emission model can estimate the emissions based on the blocking positions. In the case study, emissions increase with the vehicle volume approaching the saturated flow rate and the volumes of nonmotor vehicles and pedestrians increasing. Results show that the maximum emission increase of CO (carbon monoxide) for through phase and left-turn phase can reach 16.7% and 36.4%.

Longitudinal-Scanline-Based Arterial Traffic Video Analytics with Coordinate Transformation Assisted by 3D Infrastructure Data

High-resolution vehicle trajectory data can be used to generate a wide range of performance measures and facilitate many smart mobility applications for traffic operations and management. In this paper, a Longitudinal Scanline LiDAR-Camera model is explored for trajectory extraction at urban arterial intersections. The proposed model can efficiently detect vehicle trajectories under the complex, noisy conditions (e.g., hanging cables, lane markings, crossing traffic) typical of an arterial intersection environment. Traces within video footage are then converted into trajectories in world coordinates by matching a video image with a 3D LiDAR (Light Detection and Ranging) model through key infrastructure points. Using 3D LiDAR data will significantly improve the camera calibration process for real-world trajectory extraction. The pan-tilt-zoom effects of the traffic camera can be handled automatically by a proposed motion estimation algorithm. The results demonstrate the potential of integrating longitudinal-scanline-based vehicle trajectory detection and the 3D LiDAR point cloud to provide lane-by-lane high-resolution trajectory data. The resulting system has the potential to become a low-cost but reliable measure for future smart mobility systems.

Monte Carlo Tree Search-Based Mixed Traffic Flow Control Algorithm for Arterial Intersections

A model-free approach is presented, based on the Monte Carlo tree search (MCTS) algorithm, for the control of mixed traffic flow of human-driven vehicles (HDV) and connected and autonomous vehicles (CAV), named MCTS-MTF, on a one-lane roadway with signalized intersection control. Previous research has often simplified the problem with certain assumptions to reduce computational burden, such as dividing a vehicle trajectory into several segments with constant speed or linear acceleration/deceleration, which was rather unrealistic. This study departs from the existing research in that minimum constraints on CAV trajectory control were required, as long as the basic rules such as safety considerations and vehicular performance limits were followed. Modeling efforts were made to improve the algorithm solution quality and the run time efficiency over the naïve MCTS algorithm. This was achieved by an exploration-exploitation balance calibration module, and a tree expansion determination module to expand the tree more effectively along the desired direction. Results of a case study found that the proposed algorithm was able to achieve a travel time saving of 3.5% and a fuel consumption saving of 6.5%. It was also demonstrated to run at eight times the speed of a naïve MCTS model, suggesting a promising potential for real-time or near real-time applications.

A Particle Filter-Based Approach for Vehicle Trajectory Reconstruction Using Sparse Probe Data

Trajectory data collected from probe vehicles become increasingly important for urban traffic operation and management. However, current data tend to be sparse in time and space due to technical constraints or privacy concerns, which fail to provide a complete picture of traffic flow. This study proposes a particle filter (PF) based approach to reconstruct the vehicle trajectory for signalized arterial using sparse probe data. First, the arterial intersection is divided into multiple road cells and the estimation of cell travel time is formulated as a quadratic programming problem. Then, PF is applied to reconstruct the incomplete vehicle trajectory between consecutive updates. Specifically, to calculate and update the weight of initial particles, three measurability criteria are designed for importance sampling considering the structure of signalized arterial and the feature of vehicular updates, i.e., travel time adjustment accuracy, arterial link speed limit and travel time adjustment possibility. Last, NGSIM trajectory data are extracted at intervals to construct the sparse data, which are used to verify the effectiveness of the proposed method. The results show that reconstructed trajectories match closely with ground truth both at the single intersection and along the arterial with multiple intersections.

A two-way progression model for arterial signal coordination considering side-street turning traffic

ABSTRACTAn effective progression model should explicitly consider the turning traffic from side streets especially when the traffic volume is relatively high and the spacing between arterial intersections is short. In this paper, a new two-way bandwidth maximization approach is proposed considering left-turning traffic. The basic framework is a modified MULTIBAND model, which redefines the progression lines to more accurately consider the queueing process. We first present a bandwidth maximization model LT1, which maximizes progression bands for arterial through traffic. The features of different queue profiles are captured in the queue clearance time. Moreover, we extend the LT1 model to consider progression bands for the side-street left-turning traffic, referred to as LT2. For the lead left-turn pattern, a minimum bandwidth constraint is introduced based on an approximate delay model. Numerical results show that the proposed model can effectively reduce the overall network average delay and number of stops per vehicle.

Incorporating insights from signal optimization into reservation-based intersection controls

Reservation-based intersection control for autonomous vehicles has the potential to make greater use of intersection capacity. Indeed, previous studies on the first-come-first-served (FCFS) policy (which prioritizes vehicles by order of their reservation request) have shown improvements over optimized signals. However, in certain situations, such as asymmetric intersections, FCFS easily performs worse than signals. To address this issue, we propose two new reservation policies, WEIGHTED and PHASED. WEIGHTED weights vehicle delay by signal timings, and PHASED simulates a signal but allows red phase turning movements that will not cause a collision. We test these policies on a city network and an arterial bottleneck intersection subnetwork and show that PHASED performs better than WEIGHTED in some scenarios, and vice versa. Furthermore, we show that using a combination of PHASED and WEIGHTED can perform better than using either one alone for the entire network. Results show that these policies provide effective and easily implemented alternatives to FCFS for reservations.