Authors: Zichuan Li, Gang-Len Chang, and Suhasini Natarajan
Conference: The 11th International IEEE Conference on Intelligent Transportation System, October 2008 in Beijing, China
Status: Presented
Abstract:
Ramp metering is a very important method to improve freeway traffic system performance, but current researches emphasize only on the entering flow control, and not much attention has been paid to the exiting volume at offramp. In some situations, the exiting queue will decrease the overall system performance by a large magnitude. This is demonstrated by simulation experiments and field observation. In this study, a mix integer model is proposed to optimize the arterial and off-ramp control signal timing based on cell transmission traffic propagation model. A Genetic Algorithm based solution algorithm is proposed along with a numerical case study to demonstrate the benefits of this model.
Authors: Jie Yu, Yue Liu, Gang-Len Chang, Xiao-guang Yang
Conference: Transportation Research Board 2008
Status: Presented
Abstract:
Choosing proper locations of urban transit hubs has always been one of the critical concerns facing urban transportation planning agencies in China. This study proposes a mixed integer optimal location model for urban transit hubs, with the objective to minimize the demand-weighted total travel time, when explicitly taking into account Traffic Analysis Zones (TAZs) as demand origins or destinations in a target urban area. An Integer Non-linear Programming (INLP) reformulation was developed to significantly reduce the number of variables. Bilinear constraints in the proposed INLP formulation were then re-modeled into linear functions to ensure global optimal solutions obtained. The model was successfully applied to optimize the hub locations in Suzhou Industrial Park, China, with the result of significantly improved system performance. The impacts of several critical factors, such as the number of hubs and the travel time discount coefficient on the system performance are also investigated.
Authors: Jie Yu, Gang-Len Chang, H.W. Ho and Yue Liu
Conference: The 11th International IEEE Conference on Intelligent Transportation System, October 2008 in Beijing, China
Status: Presented
Abstract:
This paper proposes a variation-based online travel time prediction approach using clustered Neural Networks with traffic vectors extracted from raw detector data as the input variables. Different from previous studies, the proposed approach decomposes the corridor travel time into two parts: 1) the base term, which is predicted by a fuzzy membership-value-weighted average of the clustered historical data to reflect the primary traffic pattern in the corridor; and 2) the variation term, which is predicted through the calibrated cluster-based artificial neural network model to capture the actual traffic fluctuation. To evaluate the effectiveness of the proposed approach, this paper has conducted intensive numerical experiments with simulated data from the microscopic simulator CORSIM. Experimental results under various traffic volume levels have revealed the potentials for the proposed method to be applied in online corridor travel time prediction.
Authors: Xianfeng Yang, Gang-Len Chang, Yang Lu, and Saed Rahwanji
Conference: 92th TRB Annual Meeting in 2013
Abstract:
Despite the increasing use of continuous flow intersections (CFI) to contend with the congestion caused by heavy through and left-turn traffic flows, a reliable and convenient tool for the traffic community to identify potential deficiencies for CFI’s geometry design are not available yet. This is due to the unique geometric feature of CFI which comprises one primary intersection and several crossover intersections. The interdependent relation between the traffic delays and queues at a CFI’s five closely-spaced intersections cannot be fully captured with the existing analysis models developed for conventional intersections. In response to such a need, this study presents a comprehensive analysis for the overall CFI delay, identifies the potential queue spillback locations, and develops a planning framework for CFI’s geometry designs. To facilitate the application of our proposed models, this paper also includes a case study of a CFI at intersection MD 4 and MD 235 conducted by Maryland State Highway Administration.
Authors: Xin Zhang and Gang-Len Chang
Abstract: In design of evacuation plans for major metropolises, it should be recognized that a potentially large number of evacuees depend either on transit or other modes for evacuation, or need to walk over a distance to access their cars. In the process of approaching some designated pick-up points or intermediate destinations, the massive number of pedestrians may incur tremendous burden to vehicles in the roadway
network. Hence, development of a simulation tool, capable of replicating the realistic road condition for both the pedestrian and vehicle flows under the chaotic situation, is an imperative task. Such a simulation
tool should be able to account for individual behaviors as well as all kinds of interactions, including conflicts between pedestrians, between vehicles, and between pedestrians and vehicles. Despite the increasing use of traffic simulation as the primary analysis tool, effective mechanisms to simulate the mixed vehicle-pedestrian flows under congested environments remains at its infancy. In this study, we attempt to address this vital subject with the Mixed-Cellular Automata (MCA) method. Our proposed simulation model has integrated the strengths of the CA method with some probabilistic functions, offering a realistic mechanism to reflect the competing and conflict interactions between vehicle and pedestrian flows. Although the development remains at its preliminary stage, our experimental results clearly indicate that failing to account for the impact of mixed flow interactions in a congested traffic system could result in far underestimate of the delay, travel time, and system throughput.
Authors: Xin Zhang and Gang-Len Chang
Conference: 90th TRB Annual Meeting in 2011
Abstract: In metropolitan areas, a potentially large number of pedestrians depend either on transit or other modes for evacuation, or need to walk a distance to access their passenger cars. In the process of
approaching some designated pick-up points for evacuation, the massive number of pedestrians may incur tremendous burden to the vehicles on the roadway network. Thus, an effective coordination between the
vehicle and pedestrian flow during the multi-modal evacuation process shall be devised. This paper presents an integrated linear model for design of routing plans for massive mixed pedestrian-vehicle flows
within the evacuation zone. The proposed model integrating the pedestrian and vehicle networks can effectively take into their potential conflicts during the evacuation, and generate the optimal routing strategies for guiding evacuees moving toward either the pick-up locations or their parking areas. An illustrating example concerning the evacuation around the M&T stadium area has been presented and seems to indicate promising properties to address the complex interactions between vehicle and pedestrian flows within the evacuation zone. Results of simulation experiments clearly indicate that the failure to account for the conflict movement will yield unrealistic plans that can’t be implemented during the actual evacuation process.
Authors: Woon Kim, Yue Liu and Gang-Len Chang
Conference: 90th TRB Annual Meeting in 2011
Abstract:
This paper presents a detour decision model for freeway non-recurrent congestion management, based on the results of extensive simulation experiments and operational guidelines for highway agencies. The proposed model offers a reliable and effective tool for responsible traffic operators to make consistent detour decisions in response to a detected incident. Numerical results clearly indicate that a timely and well justified detour operation can yield substantial benefits to both the driving populations and the entire community.
Authors: Woon Kim and Gang-Len Chang
Conference: ITS World Congress 2010
Abstract:
This paper presents a detour decision model for freeway non-recurrent congestion management, based on the results of extensive simulation experiments and operational guidelines for highway agencies. The proposed model offers a reliable and effective tool for responsible traffic operators to make consistent detour decisions in response to a detected incident. Numerical results clearly indicate that a timely and well justified detour operation can yield substantial benefits to both the driving populations and the entire community.
Authors: Woon Kim, Gang-Len Chang, Steven M. Rochon
Conference: The 15th World Congress on Intelligent Transport Systems, New York, 2008
Abstract:
This paper presents a methodology for developing a model for estimating and predicting incident duration and identifying variables influencing the incident duration in the state of Maryland. The incident information from years 2003 to 2005 from the Maryland State Highway (MDSHA) database is used for model development, and year 2006 for the model validation. Classification Trees (CT) were used for a preliminary analysis to understand the influence of the variables associated with an incident. Based on the findings from CT, this study employed the Rule-Based Tree Model (RBTM) to develop the primary prediction model. The overall confidence for the estimated model was over 80% with several remarkable findings regarding the associations between factors and incident duration. Although the estimated results from RBTM are quite acceptable, supplemental models along with better quality database are required to improve the prediction accuracy for the duration of a detected incident.
Authors: Woon Kim, Suhasini Natarajan, and Gang-Len Chang
Conference: The 11th International IEEE Conference on Intelligent Transportation System, October 2008 in Beijing, China.
Status: Presented
Abstract:
This paper presents a methodology for developinga model to identify the variables influencing incident duration to estimate and predict incident duration in the state of Maryland. The incident information from years 2003 to 2005 from the Maryland State Highway (MDSHA) database was used for model development, and year 2006 for model validation. Classification Trees (CT) were employed for a preliminary analysis to understand the influence of the variables associated with an incident. Based on the findings from CT, this study employed the Rule-Based Tree Model(RBTM) to develop the primary prediction model. The overall confidence for the estimated model was over 80% with several remarkable findings regarding the association between the identified factors and incident duration. Although the estimated results from RBTM were quite acceptable, in cases where RBTM did not provide incident duration within a desirable short range, a discrete choice model was developed as a supplemental model. It is deduced that supplemental models along with better quality database are required to improve the prediction accuracy of the duration of a detected incident.