Stevanovic, Aleksandar

Traffic simulation and signal timing optimization are classified in structure into two main categories: (i) Macroscopic or Microscopic; (ii) Deterministic or Stochastic. Performance of the optimized signal timing derived by any tool is influenced by the methodology used in how calculations are executed in a particular tool. In this study, the performance of the optimal signal timing plans developed by two of the most popular traffic analysis tools, HCS and Tru-Traffic, each of them has its inbuilt objective function(s) to optimize signal timing for intersection, is compared with an ideal and an existing timing plans (base case) for the area of study using the microsimulation software VISSIM. An urban arterial with 29 intersections and high traffic in Fort Lauderdale, Florida serves as the test bed. To eliminate unfair superiority in the results, all experiments were performed under identical geometry and traffic conditions in each tool. Comparison of the optimized plans is conducted on the basis of average delay, average stopped delay, average number of stops, number of vehicles completed trips, latent delay, and latent demand from the simulated vehicle network performance evaluation results in VISSIM. The results indicate that, overall, HCS with its overall delay objective and the Tru-Traffic programs produce signal timing with comparable quality that performed similar to the un-optimized base case for most of the performance measures.

Signal retiming, or signal optimization process, has not changed much over the last few decades. Traditional procedures rely on low-resolution data and a low-fidelity modeling approach. Such developed signal timing plans always require a fine-tuning process for deployed signal plans in field, thus questioning the very benefits of signal optimization. New trends suggest the use of high-resolution data, which are not easily available. At the same time, many improvements could be made if the traditional signal retiming process was modified to include the use of medium-resolution data and high-fidelity modeling. This study covers such an approach, where a traditional retiming procedure is modified to utilize large medium-resolution data sets, high-fidelity simulation models, and powerful stochastic optimization to develop robust signal timing plans. The study covers a 28-intersection urban corridor in Southeastern Florida. Medium-resolution data are used to identify peak-hour, Day-Of-Year (DOY) representative volumes for major seasons. Both low-fidelity and high-fidelity models are developed and calibrated with high precision to match the field signal operations. Then, by using traditional and stochastic optimization tools, signal timing plans are developed and tested in microsimulation. The findings reveal shortcomings of the traditional approach. Signal timing plans developed from medium-resolution data and high-fidelity modeling approach reduce average delay by 5%-26%. Travel times on the corridor are usually reduced by up to 10.5%, and the final solution does not transfer delay on the other neighboring streets (illustrated through latent delay), which is also decreased by 10%-49% when compared with the traditional results. In general, the novel approach has shown a great potential. The next step should be field testing and validation.

Nowadays, there is increasing number of facilities that implement various pricing strategies in order to manage increasing traffic demand. Most of these strategies use traffic data collected on several points in the system, aggregate them in certain aggregation interval and calculate tolls based on them. Some strategies derive performance measures (as traffic density) based on aggregated data, and define tolls. However, derived performance measures tend to underestimate traffic conditions and data aggregation interval can smooth traffic data. On the other hand, travel time has not been utilized in order to calculate user tolls on such systems, and yet it can directly measure users delay in the system, and directly capture field conditions. In addition, technology to collect travel times is becoming more popular and used in transportation systems. Hence, this study aims to test alternative methods for toll calculation that will rely on travel time data and compare their performance with currently utilized toll calculation algorithm on I-95 Express lanes in South Florida.

At most of the US signal, pedestrian walk timings run in concurrence with relevant
vehicular traffic signal phases which means that major-street coordinated operations can
be interrupted by a pedestrian call. Such interruption may increase delays and stops for
major traffic flows. An alternative to this design is to increase the cycle length and embed
pedestrian timings within the ring-barrier structure of the prevailing coordination plan.
Both approaches have advantages and disadvantages. This study attempts a novel approach
to address this situation by a comprehensive experimental evaluation of traffic performance
under various pedestrian signal timing strategies. Findings show that either
abovementioned approach works well for very low traffic demands. When the traffic
demand increases findings cannot be generalized as they differ for major coordinated
movements versus overall network performance. While coordinated movements prefer no
interruption of the coordinated operations, the overall network performance is better in the
other case.

Vehicle travel time on arterial roads with signalized intersections is an essential parameter for traffic
management. There is an increasing interest in signal performance measurement for signalized
intersections in the United States. Reducing the journey times and increasing the capacity are some of
the fundamental aims with potential benefits in environmental pollution, and energy utilization. The
Travel-time Based Signal Performance Measures application is a tool for estimating signal performance
measures based on upstream-link travel times. The application utilizes well known concept of Volume-
Delay Functions to convert measured travel times into signal performance measures. Based on this
functionality, it can estimate the performance measures for 7 signalized intersections on Glades Road,
Boca Raton, Florida. Available performance measures for the major through movements are: volumeto-
capacity ratio, Level of Service LOS, and the number of cycles to wait. The derived measures can be
graphically visualized on Google Maps. The travel time data acquisition is performed using
BlueTOAD devices. The goal is to introduce automated assessment tool, visualization and evaluation of
the intersections’ performance measures simultaneously at multiple intersections. The main objective of
this web application is to help traffic operators/engineers to evaluate performance of the signalized
corridors exploiting the archived measured travel times.

Research on connected vehicles (CV) has attracted attention in the last decade due
to numerous potential applications and challenges related to exchange of information
between the vehicles (and infrastructure). Most of the relevant studies focus on these
applications and challenges with the help of novel or existing simulation frameworks. The
simulation framework often contains the mobility and communication components, and
these components are frequently simplified. In this study, the authors aim to provide the
detailed information for developing a fully V2X capable infrastructure within the lab
environment. The physical components of the proposed infrastructure include: (i) userdriven
Driving Simulator (DS) with the embedded micro-simulation tool (MS); (ii) external
traffic signal controller (TSC); (iii) Road Side Unit (RSU) and omnidirectional antenna
attached to RSU; (iv) On-Board Unit (OBU) that is integrated within DS‘s cockpit. The
proposed framework can be used for advanced applications in the context of connected
vehicles.

The monitoring of traffic signal systems can be of great importance for identifying problems, self-assessment, budgeting, creating the strategy for future steps, etc. Monitoring procedure was developed through a set of dashboards with relevant signal performance and reliability measures. The dashboards were created to reflect performance and reliability of a specific signal system on a weekly or monthly level. The author used data from ATMS.now signal system central software to illustrate how similar dashboards could be developed from any central software to enable operators to promptly and efficiently monitor various parameters of traffic signals. The main outcome of the study is a pair of Excel dashboards accompanied with appropriate user manual. The dashboards represent the tool for monitoring which can be helpful in the process of evaluation for traffic signal systems.

Macroscopic fundamental diagram is the concept of the highest importance in traffic flow theory used for development of network-wide control strategies. Previous studies showed that so called Arterial Fundamental Diagrams (AFDs) properly depict relationships between major macroscopic traffic variables on urban arterials. Most of these studies used detector’s occupancy as a surrogate measure to represent traffic density. Nevertheless, detector’s occupancy is not very often present in the field data. More frequently, field data from arterial streets provide performance metrics measured at the stop lines of traffic signals, which represent a hybrid of flow and occupancy. When such performance measures are used in lieu of density, the outcomes of the relationships between macroscopic fundamental variables can be confusing. This study investigates appropriateness of using degree of saturation, as a representative surrogate measure of traffic density, obtained from an adaptive traffic control system that utilizes stop-line detectors, for development of AFDs.

Today, the information (signal timings, detector extension, phase sequence, etc.)
to install traffic lights on the street are obtained from traffic software simulations
platforms, meaning that information from simulation is not tested on the field
(intersection where it will be installed) before the installation. Many installed controllers
on the street use time of day (TOD) patterns due to cheaper cost than adaptive traffic
control systems, but that is not the best solution for traffic volume changes that can occur
during the day or even a month. To improve traffic signal operation most of the traffic
signal controllers in the same corridor or zone operate in coordination mode.
Furthermore, phases need to be in coordination to achieve “green wave”. Green wave is
term used when in corridor traffic lights allow continues flow of traffic through
intersections that are coordinated.

Most U.S. urban traffic signal systems deploy multiple signal timing plans to
account for daily variability of traffic demand (i.e. morning peak, midday, afternoon
peak, off peak and night). Groups of signals (belonging to the one zone or section) along
an urban arterial, usually operate in a coordinated manner. This essentially means that
timing plans change at the same time for all the signals in the group, so as to facilitate
vehicle progression of through a series of signals. Good traffic signal timing practices
assume a certain level of monitoring and maintenance in order to guarantee that they are
efficient in servicing current traffic conditions.