Travel Behavior Classification: An Approach with Social Network and Deep Learning

2018 ◽  
Vol 2672 (47) ◽  
pp. 68-80 ◽  
Author(s):  
Yu Cui ◽  
Qing He ◽  
Alireza Khani
Keyword(s):  
Deep Learning ◽  
Social Network ◽  
Travel Behavior ◽  
Travel Demand ◽  
Similarity Measures ◽  
Detection Algorithm ◽  
Time Of Day ◽  
Working People ◽  
Matrix Similarity ◽  
Activity Matrix

Uncovering human travel behavior is crucial for not only travel demand analysis but also ride-sharing opportunities. To group similar travelers, this paper develops a deep-learning-based approach to classify travelers’ behaviors given their trip characteristics, including time of day and day of week for trips, travel modes, previous trip purposes, personal demographics, and nearby place categories of trip ends. This study first examines the dataset of California Household Travel Survey (CHTS) between the years 2012 and 2013. After preprocessing and exploring the raw data, an activity matrix is constructed for each participant. The Jaccard similarity coefficient is employed to calculate matrix similarities between each pair of individuals. Moreover, given matrix similarity measures, a community social network is constructed for all participants. A community detection algorithm is further implemented to cluster travelers with similar travel behavior into the same groups. There are five clusters detected: non-working people with more shopping activities, non-working people with more recreation activities, normal commute working people, shorter working duration people, later working time people, and individuals needing to attend school. An image of activity map is built from each participant’s activity matrix. Finally, a deep learning approach with convolutional neural network is employed to classify travelers into corresponding groups according to their activity maps. The accuracy of classification reaches up to 97%. The proposed approach offers a new perspective for travel behavior analysis and traveler classification.

scholarly journals Analysis of Time of Day Fare Discounts on Urban Mass Transit Travel Behavior, Crowding, and Waiting Time

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Xiao Guo ◽  
Huijun Sun
Keyword(s):  
Objective Function ◽  
Waiting Time ◽  
Travel Behavior ◽  
Travel Demand ◽  
Mass Transit ◽  
Time Of Day ◽  
Pressure Equalization ◽  
Schedule Delay ◽  
Urban Mass Transit ◽  
Artificial Network

Every morning, commuters select the regularly dispatched urban mass transit for traveling from a residential area to a workplace. This paper aims to find an optimal discount fare and time intervals on morning peak hour. As a direct and flexible traffic economic instrument, fares can influence commuters’ behavior. Therefore, fare discount has been proposed to regulate traffic flow in different time. Two models have been analyzed to describe it with schedule delay because of the travel demand size. The first objective function is constructed on pressure equalization when the travel demand is small. The other objective function is to minimize total waiting time when the travel demand is large. In the end, numerical examples based on an artificial network are performed to characterize fare discount models.

scholarly journals Departure Time Choice (DTC) Behavior for Intercity Travel during a Long-Holiday in Bangkok, Thailand

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Bhawat Chaichannawatik ◽  
Kunnawee Kanitpong ◽  
Thirayoot Limanond
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Multinomial Logit Model ◽  
Time Of Day ◽  
Travel Patterns ◽  
Peak Period ◽  
Intercity Travel ◽  
Departure Time ◽  
Departure Time Choice

Time-of-day (TOD) or departure time choice (DTC) has become an interesting issue over two decades. Many researches have intensely focused on time-of-day or departure time choice study, especially workday departures. However, the travel behavior during long-holiday/intercity travel has received relatively little attention in previous studies. This paper shows the characteristics of long-holiday intercity travel patterns based on 2012 New Year data collected in Thailand with a specific focus on departure time choice of car commuters due to traffic congestion occurring during the beginning of festivals. 590 interview data were analyzed to provide more understanding of general characteristics of DTC behavior for intercity travel at the beginning of a Bangkok long-holiday. Moreover, the Multinomial Logit Model (MNL) was used to find the car-based DTC model. The results showed that travelers tend to travel at the peak period when the parameters of personal and household are not so significant, in contrast to the trip-related characteristics and holiday variables that play important roles in traveler decision on departure time choice. Finally, some policies to distribute travel demand and reduce the repeatable traffic congestion at the beginning of festivals are recommended.

Coast-to-Coast Comparison of Time Use and Activity Patterns

2000 ◽  
Vol 1718 (1) ◽  
pp. 34-42 ◽  
Author(s):  
Sachin Gangrade ◽  
Krishnan Kasturirangan ◽  
Ram M. Pendyala
Keyword(s):  
San Francisco ◽  
Time Use ◽  
Travel Behavior ◽  
Travel Demand ◽  
Activity Patterns ◽  
Time Of Day ◽  
Demographic Characteristics ◽  
Use Patterns ◽  
The Individual ◽  
End Times

Activity-based travel analysis has been gaining increasing attention in travel demand research during the past decade. Activity and trip information collected at the person level aids in understanding the underlying behavioral patterns of individuals and the interactions among their activities and trips. Activity and time use patterns across geographical contexts are compared. Such a comparison could shed light on the differences and similarities in travel behavior that exist between areas. To accomplish this objective, activity, travel, and time use information derived from surveys conducted in the San Francisco Bay and Miami areas has been analyzed to identify differences in activity engagement patterns across different sample groups. In general, it was found that activity and time use patterns are comparable across the two areas as long as the commuting status and demographic characteristics of the individuals are controlled for. In addition, the time-of-day distributions of various events such as wake-up time, sleeping time, time of departure and arrival at home, and work start and end times were compared. These events were considered important in defining the temporal constraints under which people exercise activity and travel choices. Once again, it was found that the distributions followed similar trends as long as the commuting status and the demographic characteristics of the individual were controlled for. However, there were noticeable differences that merit further investigation.

Analyzing Cell Phone Location Data for Urban Travel

2015 ◽  
Vol 2526 (1) ◽  
pp. 126-135 ◽  
Author(s):  
Serdar Çolak ◽  
Lauren P. Alexander ◽  
Bernardo G. Alvim ◽  
Shomik R. Mehndiratta ◽  
Marta C. González
Keyword(s):  
Travel Behavior ◽  
Rio De Janeiro ◽  
Cell Phone ◽  
Travel Demand ◽  
Census Data ◽  
Low Cost ◽  
Time Of Day ◽  
Raw Data ◽  
Home Based ◽  
Demand Models

Travelers today use technology that generates vast amounts of data at low cost. These data could supplement most outputs of regional travel demand models. New analysis tools could change how data and modeling are used in the assessment of travel demand. Recent work has shown how processed origin–destination trips, as developed by trip data providers, support travel analysis. Much less has been reported on how raw data from telecommunication providers can be processed to support such an analysis or to what extent the raw data can be treated to extract travel behavior. This paper discusses how cell phone data can be processed to inform a four-step transportation model, with a focus on the limitations and opportunities of such data. The illustrated data treatment approach uses only phone data and population density to generate trip matrices in two metropolitan areas: Boston, Massachusetts, and Rio de Janeiro, Brazil. How to label zones as home- and work-based according to frequency and time of day is detailed. By using the labels (home, work, or other) of consecutive stays, one can assign purposes to trips such as home-based work. The resulting trip pairs are expanded for the total population from census data. Comparable results with existing information reported in local surveys in Boston and existing origin–destination matrices in Rio de Janeiro are shown. The results detail a method for use of passively generated cellular data as a low-cost option for transportation planning.

scholarly journals Performance Comparison of Deep Learning Autoencoders for Cancer Subtype Detection Using Multi-Omics Data

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2013
Author(s):  
Edian F. Franco ◽  
Pratip Rana ◽  
Aline Cruz ◽  
Víctor V. Calderón ◽  
Vasco Azevedo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Fusion ◽  
Similarity Measures ◽  
Research Problem ◽  
Optimal Number ◽  
Performance Comparison ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Omics Data ◽  
Cancer Subtype

A heterogeneous disease such as cancer is activated through multiple pathways and different perturbations. Depending upon the activated pathway(s), the survival of the patients varies significantly and shows different efficacy to various drugs. Therefore, cancer subtype detection using genomics level data is a significant research problem. Subtype detection is often a complex problem, and in most cases, needs multi-omics data fusion to achieve accurate subtyping. Different data fusion and subtyping approaches have been proposed over the years, such as kernel-based fusion, matrix factorization, and deep learning autoencoders. In this paper, we compared the performance of different deep learning autoencoders for cancer subtype detection. We performed cancer subtype detection on four different cancer types from The Cancer Genome Atlas (TCGA) datasets using four autoencoder implementations. We also predicted the optimal number of subtypes in a cancer type using the silhouette score and found that the detected subtypes exhibit significant differences in survival profiles. Furthermore, we compared the effect of feature selection and similarity measures for subtype detection. For further evaluation, we used the Glioblastoma multiforme (GBM) dataset and identified the differentially expressed genes in each of the subtypes. The results obtained are consistent with other genomic studies and can be corroborated with the involved pathways and biological functions. Thus, it shows that the results from the autoencoders, obtained through the interaction of different datatypes of cancer, can be used for the prediction and characterization of patient subgroups and survival profiles.

scholarly journals High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

2021 ◽  
Vol 13 (10) ◽  
pp. 1909
Author(s):  
Jiahuan Jiang ◽  
Xiongjun Fu ◽  
Rui Qin ◽  
Xiaoyan Wang ◽  
Zhifeng Ma
Keyword(s):  
Deep Learning ◽  
Gpu Computing ◽  
Hot Spot ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Sar Image ◽  
Marine Monitoring ◽  
Ship Detection

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

scholarly journals Deep Learning-Based Congestion Detection at Urban Intersections

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2052
Author(s):  
Xinghai Yang ◽  
Fengjiao Wang ◽  
Zhiquan Bai ◽  
Feifei Xun ◽  
Yulin Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical Flow ◽  
Traffic Congestion ◽  
Detection Algorithm ◽  
Input Image ◽  
Vehicle Speed ◽  
Position Information ◽  
Traffic State ◽  
State Discrimination ◽  
Discrimination Method

In this paper, a deep learning-based traffic state discrimination method is proposed to detect traffic congestion at urban intersections. The detection algorithm includes two parts, global speed detection and a traffic state discrimination algorithm. Firstly, the region of interest (ROI) is selected as the road intersection from the input image of the You Only Look Once (YOLO) v3 object detection algorithm for vehicle target detection. The Lucas-Kanade (LK) optical flow method is employed to calculate the vehicle speed. Then, the corresponding intersection state can be obtained based on the vehicle speed and the discrimination algorithm. The detection of the vehicle takes the position information obtained by YOLOv3 as the input of the LK optical flow algorithm and forms an optical flow vector to complete the vehicle speed detection. Experimental results show that the detection algorithm can detect the vehicle speed and traffic state discrimination method can judge the traffic state accurately, which has a strong anti-interference ability and meets the practical application requirements.

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