Modeling Passenger Behavior in Nonpayment Areas at Rail Transit Stations

2015 ◽  
Vol 2534 (1) ◽  
pp. 101-108 ◽  
Author(s):  
Mingjun Liao ◽  
Gang Liu
Keyword(s):  
Choice Model ◽  
Transition Probability ◽  
Simulation Models ◽  
Critical Issue ◽  
Microscopic Simulation ◽  
Movement Model ◽  
New Ideas ◽  
Path Navigation ◽  
Pass Through ◽  
Passenger Behavior

The nonpayment area at urban transit stations in China usually becomes extremely crowded during peak hours because many passengers queue to buy tickets and pass through the fare gates. How to evaluate the performance of these activities is a critical issue for the design and management of the nonpayment area. This study used microscopic simulation models to investigate passenger behavior in the nonpayment area. The study developed a queue choice model, a passenger movement model, and a path navigation model. Some new ideas were involved. First, the study introduced the concepts of dynamic queue length and dynamic distance between the current passenger and alternative queues into the queue choice model. Second, a new factor, called direction of goal, was proposed to navigate a passenger through the dynamic end of a queue or other goals. This factor was used to construct the transition probability function of a cellular automata model. Finally, the proposed models were calibrated and verified on the basis of a field survey and sensitivity analysis. The results show that the proposed models can capture passenger behaviors in the nonpayment area and perform well for queue estimation.

Simulating Indoor Evacuation of Pedestrians: The Sensitivity of Predictions to Directional-Choice Calibration Parameters

2018 ◽  
Vol 2672 (1) ◽  
pp. 171-182 ◽  
Author(s):  
Milad Haghani ◽  
Majid Sarvi ◽  
Abbas Rajabifard
Keyword(s):  
Choice Model ◽  
Simulation Models ◽  
Microscopic Simulation ◽  
Evacuation Time ◽  
Mass Gatherings ◽  
Terror Attacks ◽  
Evacuation Routes ◽  
Analytical Tools ◽  
Parameter Values ◽  
Average Individual

The increasing occurrence of safety-related incidents like fire and terror attacks in crowded public facilities and mass gatherings has heightened the importance of planning for efficient evacuations through optimizing evacuation routes and architectural designs. This calls for the development of simulation and analytical tools that can replicate occupants’ responses and thereby their most likely movement patterns. Such models must be accurate to prevent inappropriate design and planning. One major factor connected to prediction accuracy is the sensitivity of modeling outputs to the value of their various parameters. We report on implementation of a calibrated model of directional choices in a microscopic simulation model of pedestrians’ evacuation. We show how estimates of the aggregate measures of prediction are sensitive to the parameters of this tactical level (i.e., directional choice) model. Results demonstrate that the prediction of the total evacuation time and average individual evacuation times are closely correlated with one another in terms of their variation, and are both very sensitive to the specification of each directional choice parameter. Simulated evacuation time could vary up to nearly 30% depending on parameter values. The observed sensitivity highlighted the significance of importing well-calibrated parameters into such simulation models and practicing consistent degrees of accuracy for all levels of decision modeling. We also inferred that the two aggregate measures (i.e., total evacuation time and average individual evacuation times) can be used interchangeably as the basis for evacuation optimization or sensitivity analysis practices.

Modeling and Estimation of Headway Distributions

1997 ◽  
Vol 1591 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Serge P. Hoogendoorn ◽  
Hein Botma
Keyword(s):  
Estimation Method ◽  
Simulation Models ◽  
Independent Random Variables ◽  
Microscopic Simulation ◽  
Simple Analysis ◽  
Rural Roads ◽  
The Road ◽  
Road Capacity ◽  
Measurement Location ◽  
Fourier Series Analysis

A simple analysis to derive Branston’s generalized queueing model for (time-) headway distributions is presented. It is assumed that the total headway is the sum of two independent random variables: the empty zone and the free-flowing headway. The parameters of the model can be used to examine various characteristics of both the road (e.g., capacity) and driver-vehicle combinations (e.g., following behavior). Furthermore, the model can be applied to vehicle generation in microscopic simulation models and to safety analysis. To estimate the different parameters in the model, a new estimation method is proposed. This method, which was developed on the basis of Fourier-series analysis, was successfully applied to measurements collected on two-lane rural roads. The method was found to be both computationally less demanding and more robust than traditional parameter techniques procedures, such as maximum likelihood. In addition, the method provides more accurate results. Parameters in the model were examined with the developed estimation method. Estimates of these parameters at a specific period and a specific measurement location were to some extent transferable to other periods and locations. Application of the method to road capacity estimation is discussed.

Parameter Sensitivity Analysis of a Cooperative Dynamic Bus Lane System With Connected Vehicles

2021 ◽  
pp. 036119812110357
Author(s):  
Meng Xie ◽  
Michael Winsor ◽  
Tao Ma ◽  
Andreas Rau ◽  
Fritz Busch ◽  
...  
Keyword(s):  
Travel Time ◽  
Transition Period ◽  
Penetration Rate ◽  
Simulation Models ◽  
Driving Behavior ◽  
Connected Vehicles ◽  
Microscopic Simulation ◽  
Communication Range ◽  
Bus Lane ◽  
The Impact

This paper aims to evaluate the sensitivity of the proposed cooperative dynamic bus lane system with microscopic traffic simulation models. The system creates a flexible bus priority lane that is only activated on demand at an appropriate time with advanced information and communication technologies, which can maximize the use of road space. A decentralized multi-lane cooperative algorithm is developed and implemented in a microscopic simulation environment to coordinate lane changing, gap acceptance, and car-following driving behavior for the connected vehicles (CVs) on the bus lane and the adjacent lanes. The key parameters for the sensitivity study include the penetration rate and communication range of CVs, considering the transition period and gradual uptake of CVs. Multiple scenarios are developed and compared to analyze the impact of key parameters on the system’s performance, such as total saved travel time of all passengers and travel time variation among buses and private vehicles. The microscopic simulation models showed that the cooperative dynamic bus lane system is significantly sensitive to the variations of the penetration rate and the communication range in a congested traffic state. With a CV system and a communication range of 150 m, buses obtain maximum benefits with minimal impacts on private vehicles in the study simulation. The safety concerns induced by cooperative driving behavior are also discussed in this paper.

Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers

2019 ◽  
Vol 2673 (12) ◽  
pp. 367-379 ◽  
Author(s):  
Yalda Rahmati ◽  
Mohammadreza Khajeh Hosseini ◽  
Alireza Talebpour ◽  
Benjamin Swain ◽  
Christopher Nelson
Keyword(s):  
Autonomous Vehicles ◽  
Simulation Models ◽  
Driving Behavior ◽  
Data Driven ◽  
Microscopic Simulation ◽  
Car Following ◽  
The Road ◽  
Human Driver ◽  
Significant Difference ◽  
On The Road

Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.

Core Models for State-of-the-Art Microscopic Traffic Simulation and Kay Elements for Applications

2011 ◽  
pp. 92-124 ◽  
Author(s):  
Heng Wei
Keyword(s):  
Model Calibration ◽  
Traffic Simulation ◽  
State Of The Art ◽  
Choice Model ◽  
Microscopic Simulation ◽  
Calibration And Validation ◽  
Building Models ◽  
Car Following Model ◽  
Vehicle Trajectory ◽  
Model Calibration And Validation

This chapter summarizes fundamental models for microscopic simulation (such as vehicle generation model and car-following model) and other critical models (such as lane-choice model, lane-changing model, and route-choice model). Most of the critical models introduced in this chapter reflect the latest research results by the author. The primary purpose of this chapter is to provide fundamentals for better understanding of the travel behaviors that are modeled for traffic simulations. To facilitate the applications of traffic simulation models, several key elements for applying state-of-the-art computer traffic simulation tools are summarized. They include the procedure for building models, model calibration and validation. Further more, techniques for collecting vehicle trajectory data, critical elements used for model calibration and validation, are also introduced.

UNRAVELING MARKOV PROCESSES IN MOVEMENT PATTERNS OF INDICATOR SPECIES IN RESPONSE TO CHEMICAL STRESSORS

2011 ◽  
Vol 25 (12n13) ◽  
pp. 1143-1149 ◽  
Author(s):  
TUYEN VAN NGUYEN ◽  
YUEDAN LIU ◽  
IL-HYO JUNG ◽  
TAE-SOO CHON ◽  
SANG-HEE LEE
Keyword(s):  
Markov Processes ◽  
Transition Probability ◽  
Movement Patterns ◽  
Critical Issue ◽  
Transition Probability Matrix ◽  
Behavioral Changes ◽  
Environmental Stressors ◽  
Biological Responses ◽  
Low Concentrations ◽  
Before And After

Revealing biological responses of organisms in responding to environmental stressors is the critical issue in contemporary ecological sciences. Markov processes in behavioral data were unraveled by utilizing the hidden Markov model (HMM). Individual organisms of daphnia (Daphnia magna) and zebrafish (Danio rerio) were exposed to diazinon at low concentrations. The transition probability matrix (TPM) and the emission probability matrix (EPM) were accordingly estimated by training with the HMM and were verified before and after the treatments with 10-6 tolerance in 103 iterations. Structured property in behavioral changes was accordingly revealed to characterize dynamic processes in movement patterns. Parameters and sequences produced through the HMM training could be a suitable means of monitoring toxic chemicals in environment.

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