Lianyu Chu

A calibration procedure for microscopic traffic simulation

Simulation modeling is an increasingly popular and effective tool for analyzing transportation problems that are not amendable to study by other means. For any simulation study, model calibration is a crucial step to obtaining any results from analysis. This paper presents a systematic, multistage calibration and validation procedure for microscopic simulation models. The procedure is demonstrated in a calibration study with a corridor network in the southern California. The model validation results for the study network are also summarized.

Using Microscopic Simulation to Evaluate Potential Intelligent Transportation System Strategies Under Nonrecurrent Congestion

A microsimulation method is presented for evaluating the effectiveness of potential intelligent transportation system (ITS) strategies under nonrecurrent congestion. The evaluated ITS strategies include incident management, adaptive ramp metering, traveler information systems, arterial management, and a combination of those strategies. These strategies are implemented and evaluated over a road network in Irvine, California, with the microsimulation model PARAMICS. The evaluation results show that all ITS strategies have positive effects on network performance. Because of the network topology (one major freeway with two parallel arterial streets), real-time traveler information has the greatest benefits among all single ITS strategies. However, a combination of ITS strategies can further increase benefits.

Bottleneck Identification and Calibration for Corridor Management Planning

Corridor mobility improvements require a new approach to corridor management planning and operations. Recent investigations are aimed at improving the safety and efficiency of existing transportation systems by integrating state-of-the-art operational analysis (such as microsimulation) into more traditional corridor planning. One of the important elements in developing corridor management improvements is better bottleneck analysis. Such analyses play a crucial role in corridor management planning for both performance assessment and simulation model calibration. New approaches are proposed for bottleneck identification and calibration in simulation. Identification is conducted with percentile speeds based on data from multiple days. It turns out that this method is more appropriate for urban congested freeways than use of single-day data. The algorithm for bottleneck calibration represents the first attempt to rigorously calibrate bottlenecks in microsimulation. It is a three-step process–including visual assessment, bottleneck area matching, and detailed speed calibration–aiming to calibrate bottlenecks in three levels of detail. With the I-880 corridor network in the San Francisco Bay Area of California, it has been shown that the identification method can adequately identify corridor bottlenecks; the calibration procedure complements and improves the current practice of simulation calibration.

Bayesian Mixture Model for Estimating Freeway Travel Time Distributions from Small Probe Samples from Multiple Days

This study formulates a hierarchical Bayesian mixture model for estimating travel time distributions along freeway sections by using small data samples from vehicle probes, which have been collected over multiple days. Two normal components are used to capture the heterogeneity in the experienced travel times and to model various distributional shapes generally known to be skewed or multimodal. Travel time data collected during different intervals under similar traffic conditions are used to construct the prior for model parameters via a hierarchical Bayesian formulation. The posterior distributions can be continuously updated as new data from probes become available, and are used for prediction under different levels of data availability. A simulation study shows that true travel time distribution for each section during each interval can be well-approximated with the use of this proposed model.

Performance evaluation of ITS strategies using microscopic simulation

This work presents a micro-simulation method to evaluate the effectiveness of potential ITS strategies under the incident scenarios. The evaluation is conducted over a corridor network located at the City of Irvine, California. The potential ITS strategies include incident management, local adaptive ramp metering, coordinated ramp metering, traveler information systems, and a combination of the above. Based on the calibrated simulation model, we implement and evaluate these scenarios in the microscopic simulation model, PARAMICS. The evaluation results show that all ITS strategies have positive effects on the network performance. Due to the network topology (one major freeway with two parallel arterial streets), real-time traveler information system has the greatest benefits among all single ITS components. The combination of several ITS components, such as integrated control, can generate better benefits.

Sequential Modeling Framework for Optimal Sensor Placement for Multiple Intelligent Transportation System Applications

Traffic sensors have been deployed for decades to freeways to meet the requirements of various traffic/transportation applications, most noticeably traffic control and traveler information applications. A unique feature of traffic sensor deployment is that it is a continuous and evolving process. That is, with new applications that emerge, additional sensors are usually required to be deployed to meet new requirements. This process is also sequential in nature and the new deployment has to consider existing sensors. In this paper, we propose a modeling framework to capture this sequential decision-making process for traffic sensor deployment. The framework is based on our recent findings that (1) optimal sensor deployment for a single application can be determined by a staged process or, more formally, a dynamic programming (DP) method and (2) new sensor locations for new applications can be optimally solved by the DP method via considering existing sensors. We illustrate the framework using two applicati...

Policy Implications of Incorporating Hybrid Vehicles into High-Occupancy Vehicle Lanes

Abstract High-occupancy vehicle (HOV) lanes have been regarded as a cost-effective and environmental friendly option to help move people along congested routes. In spite of wide adaptation of policies, the effectiveness of HOV systems has been criticized for its under-utilization. A California statewide policy that allows hybrid vehicles to use HOV lanes was adopted under the expectation that vehicular emissions would be reduced by encouraging drivers to use fuel efficient vehicles as well traffic congestion would be eased through the more efficient use of the reserved capacity on the HOV lanes. To test the validity of this expectation, the impacts of the policy on the freeway network in Orange County, California was investigated using a method that combines a traditional planning model for demand estimation and analysis with a calibrated microscopic simulation model for accurate measures of system performance. The policy was analyzed in terms of overall system performance, corridor level performance and air quality. The key findings from this study are that the policy can be expected to have significant negative impact on HOV lanes that do not have reserve capacity. The maximum number of hybrid vehicles that the Orange County HOV system can absorb without significant degradation is about 50,000, and within this limitation, the policy can be expected to be successful in reducing emissions by allowing hybrid vehicles into HOV lanes.

GA-based parameter optimization for the ALINEA ramp metering control

ALINEA, a local feedback ramp-metering strategy, has been shown to be a remarkably simple, highly efficient and easy application. This paper presents a microscopic simulation-based method to optimize the operational parameters of the algorithm, as an alternative to the difficult task of fine-tuning them in real-world testing. Four parameters, including the update cycle of the metering rate, a constant regulator, the location and desired occupancy of the downstream detector station, are considered. A genetic algorithm that searches the optimal combination of parameter values is employed. Simulation results show that the genetic algorithm is able to find a set of parameter values that can optimize the performance of the ALINEA algorithm.

Development of Methods and Tools for Managing Traffic Congestion in Freeway Corridors

In this paper we present some of our research findings derived from a series of research activities funded by the California PATH program to commemorate the occasion of the establishment of the PATH program 20 years ago. The major theme woven by these research efforts is the development of more effective traffic management tools that help tame unbridled traffic congestion in California, and the major contributions include a better understanding of traveler behavior, improved methods for obtaining origin-destination demand matrices, and increased modeling and control capabilities

Traffic Semantic Analysis Based on Mobile Phone Base Station Data

As traffic sensors gradually increase, traffic managers obtain more and more detection data, and the data volume has jumped to the Big Data magnitude. Recently, the cell detail record data are used as an emerging traffic detection data source. Using mobile phone as a probe, its detection data is able to well reflect users travel behavior. Meanwhile, cell phone base stations can be treated as fixed sensor and used to detect people flows in the base station area, reflect the distribution of traffic source, and provide supports for the division of the commuting traffic zone. In this article, the traffic semantic framework is proposed. We analyze the data of cell detail record data in Beijing, and extract four features of base stations: real-time user stock, inflow, outflow and increments, to tag the traffic semantic attribute of the base stations.