Journal of Advanced Transportation

Volume 2019, Article ID 2751916, 12 pages

https://doi.org/10.1155/2019/2751916

## Determination of Bus Crowding Coefficient Based on Passenger Flow Forecasting

^{1}School of Traffic & Transportation Engineering, Dalian Jiaotong University, Dalian 116028, China^{2}Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY 82071, USA^{3}College of Mechanical Engineering, Dalian Jiaotong University, Dalian 116028, China

Correspondence should be addressed to Zhongyi Zuo; nc.ude.utjd@yzouz

Received 28 August 2018; Revised 12 January 2019; Accepted 3 February 2019; Published 1 April 2019

Guest Editor: Marcos M. Vega

Copyright © 2019 Zhongyi Zuo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

To improve bus passengers’ degree of comfort, it is necessary to determine the real-time crowd coefficient in the bus. With this concern, this paper employed the RBF Neural Networks approach to predict the number of passengers in the bus based on historical data. To minimize the impact of the randomness of passenger flow on the determination of bus crowd coefficient, a cloud model-based bus crowd coefficient identification method was proposed. This paper first selected the performance measurements for determining bus crowd coefficient and calculated the digital characteristics of the cloud model based on the boundary values of the selected performance measures under six Levels-of-Service (LOSs). Then the subclouds obtained under the six LOSs were synthesized into a standard cloud. According to the predicted number of passengers in the bus, the passenger density and loading frequency were calculated, which were imported into the cloud generator to set up the bus crowd coefficient identification model. By calculating the crowd degrees of identification cloud and template cloud at each site, this paper determined the crowed coefficient of each bus station. Finally, this paper took the bus line No. 10 in Dalian city as case study to verify the proposed model. It was found that the crowd coefficients of the selected route ranged from 60.265 to 109.825, and the corresponding LOSs ranged between C and F. The method of discriminating bus crowding coefficient can not only effectively determine the congestion coefficient, but also effectively avoid the fuzziness and randomness of the crowding coefficient judgment in the bus, which has strong theoretical and practical significance.

#### 1. Introduction

With the development of urbanization and the rapid growth of urban population, traffic congestion problems have seriously restricted the nation’s economic development and affected people’s daily lives. In the process of development, we should give priority to the development of public transportation and improve the service quality and travel environment of public transportation. Developing public transportation vigorously is an important way to alleviate road traffic congestion [1]. At present, we usually use standing-passenger density and loading frequency to judge the bus congestion coefficient in the domestic and foreign countries. The standing-passenger density can not only affect the passengers’ stress, but also seriously affect the personal safety of the passengers in the bus if the standing-passenger density exceeds the normal values. Lu et al. [2] analyzed the crowded people and found that, in the case of high standing-passenger density, there is a large squeeze force between the passenger and the passenger. In addition, the accumulation and spread of the force ar prone to causing traffic accidents. Ran et al. [3] explored the population density and found that the limit of the density of the Chinese population was 9 m^{−2} when the crowded accident occurred according to the individual physiological size. Liu et al. [4] analyzed the various warning indicators and optimized the population density as warning indicator of the degree of crowding in the bus. At last, she proposed the recommended value of the population density according to different situations. Chen et al. [5] used survey, simulation, and other means to select passenger flow density as a parameter indicator and passenger flow as a weight to construct a passenger flow congestion index suitable for assessing the degree of congestion, and the congestion index is at . He et al. [6] proposed the fact that the congestion degree is the time characteristic of pedestrians gathering in response to passenger evacuation and can respond to the comfort during passenger evacuation, which is determined by the number of passengers per unit area. Jiang et al. [7] used the standing-passenger density to describe the congestion degree and adopted the RP_SP method to establish a crowding degree grading system based on standing-passenger density. Xu [8] used the service level based on personal space demand to measure the congestion level of pedestrian facilities and established a crowd perception function based on neuron model. At last the paper proposed a congestion perception information representation method based on spatial environment information representation. In addition, some scholars used image processing technology for passenger flow statistics and the method had also achieved certain development. In 2008, in order to achieve passenger area detection function, Yu et al. [9] proposed a new foreground/background edge model (FBEM) detection method, which traversed all the pixels in the video image and counted statistics and learning to obtain the background area and foreground area within the image. In 2014, Tian [10] used the background difference algorithm and the closed contour fitting moving target detection algorithm in the video detection process to extract the passenger contour by using the morphological processing method, and she also used the head and shoulder classifier to count the passenger movement direction trajectory to realize the passenger detection function. In 2010, Chen [11] performed passenger edge detection in the RGB color space and then used the Hough transform to calibrate the passenger's head area. Finally, the MeanShift's target tracking was used to complete the bus passenger movement target count. In 2013, Hou [12] extracted the passenger's head area by using information such as Hough circle detection and confidence gray interval and combined with the CamShift target tracking algorithm predicted by Kalman filtering. At the same time, the images collected by the upper and lower door cameras are analyzed to calculate the passengers.

Some scholars employed video image processing technology to count the number of people in the bus, and they have achieved certain achievements. For instance, Mukherjee et al. [13] used the Hough circle to extract the passenger's head geometry and count the passengers by the number of blocks that match the set feature. In 2012, Garcia-Bunster et al. [14] corrected the viewing angle of the image and combined it with the standard linear regression model and linear discriminant parameters to find the mapping between the optimal area measurement and the count. Finally, they applied this method to queued passenger counts. In 2013, Daley et al. [15] used the infrared light of the appropriate wavelength to detect the passenger situation in the seating area and the passage area of the vehicle and realized the counting function of the passengers in the bus according to the geometric distribution of the vehicle and the passenger. Mudoi et al. [16] used the background difference method to extract the target region in the video image, and combined with the neural network algorithm. They used the neural network algorithm to train the results to identify the object in the color and shape characteristics of the target. In 2013, Miklasz et al. [17] used facial recognition algorithm combined with passenger flow optical analysis technology to realize the statistics of passenger flow technology in the car, and the results in the experiment proved that the method has very high statistical accuracy. In 2000, Feng et al. proposed Discrete Representation Method (DRM), which is to analyze the sequence of object trigger points by analyzing discrete targets and object centerlines. This method solved the problem of overlapping objects in passenger counting research [18]. In 2008, Yahiaoui et al. simulated the stereo surveillance video sequence on the bus and the algorithm achieved 99% accuracy by passenger counting experiment [19].

The above research mainly determines the bus crowding coefficient from the two aspects, namely, standing-passenger density and the actual number of people in the bus. The discriminant index is single and has certain fuzziness and randomness. In view of this, this paper proposes a method for judging the bus crowding coefficient based on passenger flow data by using the cloud model. And this method combines the standing-passenger density and loading frequency to identify the crowding coefficient. The method of cloud model can not only avoid fuzziness and randomness of traditional method but also has a strong practical effect.

The remainder of this paper is organized as follows: Section 2 discusses the model to predict the number of passengers in the bus at each bus station. Section 3 introduces the method of bus crowding coefficient based on passenger flow forecast. In this part, we introduced the cloud model to discriminate bus crowding coefficient. Section 4 provides an experimental evaluation of the proposed enhancements. Finally, conclusions of this research are presented in Section 5.

#### 2. Model Development

##### 2.1. Prediction of Passenger Flow Based on RBF Neural Network

The training method of RBF neural network is simple and efficient. Besides, it has good function approximation ability, classification learning ability, and high convergence speed. The RBF neural network can deal with various intrinsic and difficult to analyze complex system regularity problems. Compared with traditional prediction methods, the use of RBF neural network for passenger flow prediction has the following advantages.

*(1) Self-Learning Ability*. The RBF neural network can adapt to the randomness and nonlinearity of passenger flow changes between stations on public transport lines through continuous training of data. And it has strong nonlinear processing ability. It also makes up for the shortcomings of traditional forecasting methods in solving nonlinear and time-varying problems.

*(2) Adaptive and Self-Organizing Ability*. The RBF neural network can automatically adjust network parameters according to input and output samples, and it establishes a good input-output mapping relationship to achieve the prediction function.

*(3) Fault Tolerance and Self-Repairing Ability*. The RBF neural network can give correct answers to incomplete information and the system can still be in good condition when some internal faults occur. Therefore, when forecasting the number of passengers in a bus, it only provides the data of passenger flow on and off the bus to train the neural network. And the information of the distribution matrix is obtained and stored in the network. The actual situation can be predicted accurately without relying on the determined distribution matrix.

*(i) Algorithm Design*. This paper uses a three-layer neural network to predict the number of passengers in the bus. It surveyed the number of people in the bus on No. 10 in Dalian City from Monday to Friday in three weeks. Specific steps are described as follows.

*Step 1*. Collect historical passenger flow data via the Information Collection System. Then, we selected the number of passengers in the bus under normal operating conditions as the sample data. The data were divided into two subdatasets: training dataset and prediction dataset. The number of people in early rush hour in previous two weeks was trained as training dataset, and the prediction dataset was the number in early rush hour on the third Friday.

*Step 2*. To avoid the potential prediction errors that might be caused by the sample size of the collect datasets, the original data need to be normalized prior to prediction.

*Step 3*. Construct the passenger flow prediction model. The historical data of number of passengers in the bus, under various weather conditions, holidays, and weeks are selected as input variables to train the neural network and construct a predictive model.

*Step 4*. Apply the trained neural network model to predict the number of passengers in the bus at a certain time in the future.

*Step e*. Analyze prediction errors.

*(ii) Evaluation Indicators*. In order to evaluate the predict results of bus passenger traffic, this paper introduced a predictive result evaluation index. Specifically, predication errors were calculated by comparing the difference between the predicted value Y_{sim} and the actual value Y_{real.} There are four indicators for verifying the difference. Among them, the mean average error represents the deviation level between the predicted value and the actual value, and the smaller the error value is, the closer the predicted value is to the true value. The mean average relative error is a commonly used indicator for evaluating prediction results. When mean average relative error is between 20% and 50%, the prediction result is proved to be feasible.(a) Mean Average Error(b) Mean Square Error(c) Mean Average Relative Error(d) Mean Square Relative Error

#### 3. Determination of Bus Crowding Coefficient

##### 3.1. Measurement of Passengers’ Crowding Coefficient

The purpose of determining the crowding coefficient in the bus is to timely and reliably identify the passenger crowd in the bus, so as efficient measures could be applied to reduce the potential safety hazards to passengers and improve passengers’ comfort. It has been a common practice that existing research methods use fuzziness and randomness for dividing the crowding states in buses. Therefore, it is of great significance to use a reasonable method to divide the crowded state in the bus. In this paper, the standing-passenger density and loading frequency were used to determine the crowding factor in the bus, as shown in Tables 1 and 2.