Lampu Lalulintas Adaptif untuk Simpangan Padat Menggunakan Simple Additive Weight

Antrean kendaraan pada suatu simpangan akan menghambat arus pada simpangan lainnya. Lampu lalulintas yang mengatur ketertiban dan kelancaran lalulintas dapat menyebabkan kemacetan lalulintas karena kurang tepatnya pembagian durasi lampu lalulintas menyala hijau dan durasi lampu lalulintas menyala merah untuk setiap lintasan. Masalah pengaturan durasi pada setiap lampu lalu lintasan dapat diselesaikan dengan lampu lalulintas adaptif berdasarkan beberapa kriteria, yaitu jenis kendaraan, panjang antrean kendaraan, keberadaan kendaraan darurat, jumlah kendaraan, jumlah pejalan kaki, dan durasi lampu hijau telah menyala di setiap lintasan. Kriteria yang digunakan ini menjadi kriteria penentu yang digunakan dalam metode simple additive weight (SAW) untuk menentukan lampu lalulintas arah mana yang harus hijau dan seberapa lama lampu lalulintas hijau tersebut harus menyala. Pemilihan metode SAW tersebut didasarkan pada kemampuan metode tersebut dalam mengambil suatu keputusan yang tepat berdasarkan kriteria-kriteria yang telah ditentukan dan memiliki proses perhitungan yang cepat serta sederhana. Selain menggunakan metode SAW, persyaratan durasi minimal lampu lalulintas menyala hijau untuk setiap lintasan juga digunakan untuk kenyamanan pengguna jalan, baik penyeberang jalan maupun pengendara kendaraan. Dengan metode ini dalam simulasi persimpangan dengan berbagai jumlah dan jenis kendaraan, diperoleh pengurangan antrean kendaraan sebesar 28% tetapi meningkatkan perubahan pergantian lampu hijau sebesar 70% jika metode ini diterapkan untuk jangka waktu yang lama

Lampu Lalulintas Adaptif untuk Simpangan Padat Menggunakan Simple Additive Weight

Antrean kendaraan pada suatu simpangan akan menghambat arus pada simpangan lainnya. Lampu lalulintas yang mengatur ketertiban dan kelancaran lalulintas dapat menyebabkan kemacetan lalulintas karena kurang tepatnya pembagian durasi lampu lalulintas menyala hijau dan durasi lampu lalulintas menyala merah untuk setiap lintasan. Masalah pengaturan durasi pada setiap lampu lalu lintasan dapat diselesaikan dengan lampu lalulintas adaptif berdasarkan beberapa kriteria, yaitu jenis kendaraan, panjang antrean kendaraan, keberadaan kendaraan darurat, jumlah kendaraan, jumlah pejalan kaki, dan durasi lampu hijau telah menyala di setiap lintasan. Kriteria yang digunakan ini menjadi kriteria penentu yang digunakan dalam metode simple additive weight (SAW) untuk menentukan lampu lalulintas arah mana yang harus hijau dan seberapa lama lampu lalulintas hijau tersebut harus menyala. Pemilihan metode SAW tersebut didasarkan pada kemampuan metode tersebut dalam mengambil suatu keputusan yang tepat berdasarkan kriteria-kriteria yang telah ditentukan dan memiliki proses perhitungan yang cepat serta sederhana. Selain menggunakan metode SAW, persyaratan durasi minimal lampu lalulintas menyala hijau untuk setiap lintasan juga digunakan untuk kenyamanan pengguna jalan, baik penyeberang jalan maupun pengendara kendaraan. Dengan metode ini dalam simulasi persimpangan dengan berbagai jumlah dan jenis kendaraan, diperoleh pengurangan antrean kendaraan sebesar 28% tetapi meningkatkan perubahan pergantian lampu hijau sebesar 70% jika metode ini diterapkan untuk jangka waktu yang lama

Traffic Signal Controller Optimization Through VISSIM to Minimize Traffic Congestion, CO and NOx Emissions, and Fuel Consumption

In developing countries with heterogeneous traffic, such as Sri Lanka, it is possible to observe severe traffic congestion at intersections and traffic corridors. The main objective of this study was to demonstrate the optimization of traffic signal controllers using VISSIM microsimulation software. It aimed to minimize traffic congestion, emissions, and fuel consumption. This study focused on developing a traffic signal controller optimization program for a congested traffic corridor which consisted of a three-legged signalized intersection, a four-legged unsignalized intersection, and a three-legged unsignalized intersection. The entire corridor was modeled here, and the already signalized three-legged intersection was optimized. Traffic signal controller optimization was done separately through the built-in optimization features in VISSIM and Webster’s Method. The results showed that emissions and fuel consumption were reduced by 14.89 % in VISSIM optimization and 14.11% in optimization using Webster’s Method. Through the comparison between the VISSIM optimized signal timing and manually calculated signal timing, it was found that the signal timing optimization provides much more improved results than the manual signal timing calculations. Using the proposed methodology, the traffic signal controllers can be optimized within a short duration in very few steps without any iterations compared to the existing traffic signal controller optimization techniques. Therefore, the proposed methodology is a good alternative method to optimize the traffic signal controllers

A deterministic predictive traffic signal control model in intelligent transportation and geoinformation systems

В работе предлагается метод адаптивного управления сигналами/фазами светофоров в интеллектуальных транспортных и геоинформационных системах, основанный на детерминированной прогнозной модели. Под детерминированной прогнозной моделью в работе понимается набор явных аналитических закономерностей и / или операций, связывающих информацию о движении транспортных средств в окрестности конкретного перекрёстка, с данными о прогнозируемом «потоке» транспортных средств через перекрёсток за одну конкретную фазу светофорного цикла. Предлагаемый метод управления основывается на выборе фазы светофорного цикла, прогнозируемый поток для которой оказывается максимален. Таким образом, метод обеспечивает управление сигналами / фазами светофоров на основе данных о движении транспорта, включая данные с подключенных и автономных транспортных средств. Экспериментальные исследования были проведены в системе микроскопического моделирования транспортных потоков SUMO. Представлено сравнение предложенного метода с решениями, обладающими лучшими в своём классе показателями эффективности: эмпирическими алгоритмами управления и методами управления на основе обучения с подкреплением. Показано преимущество предложенного метода, определены направления дальнейших исследований.Работа выполнена при поддержке Российского научного фонда (проект № 21-11-00321, https://rscf.ru/en/project/21-11-00321/)

Traffic Control Strategy Formulation and Optimization Enabled by Homogenous Connected and Autonomous Vehicle Systems.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

Intelligent traffic control at signalized intersections in urban areas is vital for mitigating congestion and ensuring sustainable traffic operations. Poor traffic management at road intersections may lead to numerous issues such as increased fuel consumption, high emissions, low travel speeds, excessive delays, and vehicular stops. The methods employed for traffic signal control play a crucial role in evaluating the quality of traffic operations. Existing literature is abundant, with studies focusing on applying regression and probability-based methods for traffic light control. However, these methods have several shortcomings and can not be relied on for heterogeneous traffic conditions in complex urban networks. With rapid advances in communication and information technologies in recent years, various metaheuristics-based techniques have emerged on the horizon of signal control optimization for real-time intelligent traffic management. This study critically reviews the latest advancements in swarm intelligence and evolutionary techniques applied to traffic control and optimization in urban networks. The surveyed literature is classified according to the nature of the metaheuristic used, considered optimization objectives, and signal control parameters. The pros and cons of each method are also highlighted. The study provides current challenges, prospects, and outlook for future research based on gaps identified through a comprehensive literature review

Multi-Modal Traffic Signal Design under Safety and Operations Constraints

Currently, most transportation agencies design signal timing plans for intersection with the main objective of minimizing vehicular traffic delay while ensuring compliance with basic safety guidelines. Often times along urban roadways where automobiles share the space with large volumes of non-motorized users (i.e. pedestrians and cyclists), reaching a balance between delays and safety of all road users is a challenging task. In this thesis, different approaches are presented to address potential improvements on traffic operations and safety of intersections serving more than one mode of transportation. The impact of tunnels on the pedestrian operations and the effect of applying different signal timing plans on the performance of an isolated intersection are being studied. A methodology is proposed to reach a desired compromise between the safety and efficiency of either an isolated intersection or a corridor of independent/coordinated intersections. An integrated delay-safety (DS) indicator is used in combination with a neural network based tool. The proposed methodology was applied to a real-world urban arterial in downtown Montreal, along which a bicycle path was recently built. The study area was evaluated using VISSIM, a microscopic traffic simulator, by coding traffic signal timing plans along the arterial to perform independently, or coordinated. The objective is to advance with minimum delay a specified transportation mode (i.e. automobiles or bicycles). A Multi-Layer Perceptron (MLP) neural-network was built to identify what type of signal timing plan yields the best tradeoff between automobile delay and safety of non-motorized users. Based on traffic data collected from real-world and from simulations, a large date set of input/output pairs was used to train and test the MLP neural network. It was found that for 99.8% of the tested cases the neural network identifies correctly the configuration of signal timing plan that yields the optimal DS value

Integrated Approach for Diversion Route Performance Management during Incidents

Non-recurrent congestion is one of the critical sources of congestion on the highway. In particular, traffic incidents create congestion in unexpected times and places that travelers do not prepare for. During incidents on freeways, route diversion has been proven to be a useful tactic to mitigate non-recurrent congestion. However, the capacity constraints created by the signals on the alternative routes put limits on the diversion process since the typical time-of-day signal control cannot handle the sudden increase in the traffic on the arterials due to diversion. Thus, there is a need for proactive strategies for the management of the diversion routes performance and for coordinated freeway and arterial (CFA) operation during incidents on the freeway. Proactive strategies provide better opportunities for both the agency and the traveler to make and implement decisions to improve performance. This dissertation develops a methodology for the performance management of diversion routes through integrating freeway and arterials operation during incidents on the freeway. The methodology includes the identification of potential diversion routes for freeway incidents and the generation and implementation of special signal plans under different incident and traffic conditions. The study utilizes machine learning, data analytics, multi-resolution modeling, and multi-objective optimization for this purpose. A data analytic approach based on the long short term memory (LSTM) deep neural network method is used to predict the utilized alternative routes dynamically using incident attributes and traffic status on the freeway and travel time on both the freeway and alternative routes during the incident. Then, a combination of clustering analysis, multi- resolution modeling (MRM), and multi-objective optimization techniques are used to develop and activate special signal plans on the identified alternative routes. The developed methods use data from different sources, including connected vehicle (CV) data and high- resolution controller (HRC) data for congestion patterns identification at the critical intersections on the alternative routes and signal plans generation. The results indicate that implementing signal timing plans to better accommodate the diverted traffic can improve the performance of the diverted traffic without significantly deteriorating other movements\u27 performance at the intersection. The findings show the importance of using data from emerging sources in developing plans to improve the performance of the diversion routes and ensure CFA operation with higher effectiveness

Dynamic Message Sign and Diversion Traffic Optimization

This dissertation proposes a Dynamic Message Signs (DMS) diversion control system based on principles of existing Advanced Traveler Information Systems and Advanced Traffic Management Systems (ATMS). The objective of the proposed system is to alleviate total corridor traffic delay by choosing optimized diversion rate and alternative road signal-timing plan. The DMS displays adaptive messages at predefined time interval for guiding certain number of drivers to alternative roads. Messages to be displayed on the DMS are chosen by an on-line optimization model that minimizes corridor traffic delay. The expected diversion rate is assumed following a distribution. An optimization model that considers three traffic delay components: mainline travel delay, alternative road signal control delay, and the travel time difference between the mainline and alternative roads is constructed. Signal timing parameters of alternative road intersections and DMS message level are the decision variables; speeds, flow rates, and other corridor traffic data from detectors serve as inputs of the model. Traffic simulation software, CORSIM, served as a developmental environment and test bed for evaluating the proposed system. MATLAB optimization toolboxes have been applied to solve the proposed model. A CORSIM Run-Time-Extension (RTE) has been developed to exchange data between CORSIM and the adopted MATLAB optimization algorithms (Genetic Algorithm, Pattern Search in direct search toolbox, and Sequential Quadratic Programming). Among the three candidate algorithms, the Sequential Quadratic Programming showed the fastest execution speed and yielded the smallest total delays for numerical examples. TRANSYT-7F, the most credible traffic signal optimization software has been used as a benchmark to verify the proposed model. The total corridor delays obtained from CORSIM with the SQP solutions show average reductions of 8.97%, 14.09%, and 13.09% for heavy, moderate and light traffic congestion levels respectively when compared with TRANSYT-7F optimization results. The maximum model execution time at each MATLAB call is fewer than two minutes, which implies that the system is capable of real world implementation with a DMS message and signal update interval of two minutes