scholarly journals Modeling Tourists’ Departure Time considering the Influence of Multisource Traffic Information

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shijun Yu ◽  
Siyuan Zhang ◽  
Shejun Deng ◽  
Tao Ji ◽  
Peng Zhou ◽  
...  
Keyword(s):  
Travel Behavior ◽  
Multinomial Logit Model ◽  
Economic Benefits ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Traffic Information ◽  
Urban Residents ◽  
Related Factors ◽  
Departure Time ◽  
Intelligent Technology

The development of tourism brings economic benefits as well as additional pressure on the urban traffic system. For example, the travel time of tourists coincides with the rush hour of urban residents’ daily commuting. Limited urban traffic resources cannot meet the travel needs of tourists and urban residents at the same time, resulting in traffic congestion and low travel efficiency. Now, with the development of intelligent technology, tourists can obtain real-time information about transportation systems through various channels and adjust their travel behavior accordingly. This study shows tourists’ travel behavior based on a survey conducted to the tourists in Yangzhou city. 1500-interview data are analyzed, and a Multinomial Logit Model (MNL) was employed to establish the probability prediction model of tourists’ departure time choice. The results presented that sync traffic information and some other tourism-related factors determine the choice of tourists’ departure time. These factors distinguish the travel behavior of tourists from the daily travel behavior of urban residents. This study can provide suggestions for the urban tourism management department to formulate more targeted and efficient policies while creating a more comfortable tourism environment for tourists.

Travel mode recognition of urban residents using mobile phone data and MapAPI

2021 ◽  
pp. 239980832098300
Author(s):  
Zhenghong Peng ◽  
Guikai Bai ◽  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu
Keyword(s):  
Urban Planning ◽  
Mobile Phone ◽  
Traffic Flow ◽  
Road Traffic ◽  
Urban Traffic ◽  
Phone Call ◽  
Traffic Information ◽  
Mobile Phone Data ◽  
Urban Residents ◽  
Travel Mode

Obtaining the time and space features of the travel of urban residents can facilitate urban traffic optimization and urban planning. As traditional methods often have limited sample coverage and lack timeliness, the application of big data such as mobile phone data in urban studies makes it possible to rapidly acquire the features of residents’ travel. However, few studies have attempted to use them to recognize the travel modes of residents. Based on mobile phone call detail records and the Web MapAPI, the present study proposes a method to recognize the travel mode of urban residents. The main processes include: (a) using DBSCAN clustering to analyze each user’s important location points and identify their main travel trajectories; (b) using an online map API to analyze user’s means of travel; (c) comparing the two to recognize the travel mode of residents. Applying this method in a GIS platform can further help obtain the traffic flow of various means, such as walking, driving, and public transit, on different roads during peak hours on weekdays. Results are cross-checked with other data sources and are proven effective. Besides recognizing travel modes of residents, the proposed method can also be applied for studies such as travel costs, housing–job balance, and road traffic pressure. The study acquires about 6 million residents’ travel modes, working place and residence information, and analyzes the means of travel and traffic flow in the commuting of 3 million residents using the proposed method. The findings not only provide new ideas for the collection and application of urban traffic information, but also provide data support for urban planning and traffic management.

scholarly journals Sensing Urban Transportation Events from Multi-Channel Social Signals with the Word2vec Fusion Model

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4093 ◽  
Author(s):  
Hao Lu ◽  
Kaize Shi ◽  
Yifan Zhu ◽  
Yisheng Lv ◽  
Zhendong Niu
Keyword(s):  
Single Channel ◽  
Low Cost ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Traffic Information ◽  
Social Signals ◽  
Fusion Model ◽  
Online Data ◽  
Traffic Incidents ◽  
Traffic Incident

Social sensors perceive the real world through social media and online web services, which have the advantages of low cost and large coverage over traditional physical sensors. In intelligent transportation researches, sensing and analyzing such social signals provide a new path to monitor, control and optimize transportation systems. However, current research is largely focused on using single channel online social signals to extract and sense traffic information. Clearly, sensing and exploiting multi-channel social signals could effectively provide deeper understanding of traffic incidents. In this paper, we utilize cross-platform online data, i.e., Sina Weibo and News, as multi-channel social signals, then we propose a word2vec-based event fusion (WBEF) model for sensing, detecting, representing, linking and fusing urban traffic incidents. Thus, each traffic incident can be comprehensively described from multiple aspects, and finally the whole picture of unban traffic events can be obtained and visualized. The proposed WBEF architecture was trained by about 1.15 million multi-channel online data from Qingdao (a coastal city in China), and the experiments show our method surpasses the baseline model, achieving an 88.1% F1 score in urban traffic incident detection. The model also demonstrates its effectiveness in the open scenario test.

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.

scholarly journals Scalable Data Model for Traffic Congestion Avoidance in a Vehicle to Cloud Infrastructure

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5074
Author(s):  
Ioan Stan ◽  
Vasile Suciu ◽  
Rodica Potolea
Keyword(s):  
Data Structures ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Negative Impact ◽  
Range Query ◽  
Data Representation ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Traffic Information ◽  
Cloud Infrastructure

Traffic congestion experience in urban areas has negative impact on our daily lives by consuming our time and resources. Intelligent Transportation Systems can provide the necessary infrastructure to mitigate such challenges. In this paper, we propose a novel and scalable solution to model, store and control traffic data based on range query data structures (K-ary Interval Tree and K-ary Entry Point Tree) which allows data representation and handling in a way that better predicts and avoids traffic congestion in urban areas. Our experiments, validation scenarios, performance measurements and solution assessment were done on Brooklyn, New York traffic congestion simulation scenario and shown the validity, reliability, performance and scalability of the proposed solution in terms of time spent in traffic, run-time and memory usage. The experiments on the proposed data structures simulated up to 10,000 vehicles having microseconds time to access traffic information and below 1.5 s for congestion free route generation in complex scenarios. To the best of our knowledge, this is the first scalable approach that can be used to predict urban traffic and avoid congestion through range query data structure traffic modelling.

Empirical Results from Taiwan and Their Implications for Advanced Traveler Pretrip Information Systems

1997 ◽  
Vol 1607 (1) ◽  
pp. 126-133 ◽  
Author(s):  
Rong-Chang Jou ◽  
Ta-Yin Hu ◽  
Chien-Wen Lin
Keyword(s):  
Travel Behavior ◽  
Choice Behavior ◽  
Subsequent Development ◽  
Traffic Information ◽  
Change Behavior ◽  
Important Relationship ◽  
Departure Time ◽  
Home Interview ◽  
The Individual ◽  
Types Of Information

A statistical analysis of travelers’ behavior is presented, and a study into the effects of pretrip information on travelers’ choice behavior is addressed. The study is based on an extensive home interview survey conducted in the Taichung metropolitan area of Taiwan. The main objectives were (a) to determine which types of information are more important to travelers; (b) to examine whether the provision of information alters the travelers’ choice behavior; (c) to relate travelers’ choice behavior, including departure time, route, and mode, to personal and travel behavior characteristics; and (d) to provide a basis for the subsequent development of a pretrip information system architecture. A binary logit model of whether a traveler switches departure time, route, mode, or any combinations of the three or does not switch after receiving traffic information is estimated. The results underscore the important relationship between the different characteristics and the propensity of travelers to change behavior. Separate binary models are developed for each of the individual trip purposes. The focus is on the urban (intraregional) trips.

scholarly journals Linking Mode Choice with Travel Behavior by Using Logit Model Based on Utility Function

2021 ◽  
Vol 13 (8) ◽  
pp. 4332
Author(s):  
Wissam Qassim Al-Salih ◽  
Domokos Esztergár-Kiss
Keyword(s):  
Utility Function ◽  
Travel Time ◽  
Travel Behavior ◽  
Logit Model ◽  
Mode Choice ◽  
Multinomial Logit Model ◽  
Individual Characteristics ◽  
Urban Traffic ◽  
Modeling Tools ◽  
Maximum Utility

The currently available transport modeling tools are used to evaluate the effects of behavior change. The aim of this study is to analyze the interaction between the transport mode choice and travel behavior of an individual—more specifically, to identify which of the variables has the greatest effect on mode choice. This is realized by using a multinomial logit model (MNL) and a nested logit model (NL) based on a utility function. The utility function contains activity characteristics, trip characteristics including travel cost, travel time, the distance between activity place, and the individual characteristics to calculate the maximum utility of the mode choice. The variables in the proposed model are tested by using real observations in Budapest, Hungary as a case study. When analyzing the results, it was found that “Trip distance” variable was the most significant, followed by “Travel time” and “Activity purpose”. These parameters have to be mainly considered when elaborating urban traffic models and travel plans. The advantage of using the proposed logit models and utility function is the ability to identify the relationship among the travel behavior of an individual and the mode choice. With the results, it is possible to estimate the influence of the various variables on mode choice and identify the best mode based on the utility function.

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti
Keyword(s):  
Information System ◽  
Real Time ◽  
Travel Demand ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Information ◽  
Estimation Methods ◽  
Traffic Counts ◽  
Real Time Traffic ◽  
Demand Models

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

scholarly journals Commuter Response to Traffic Information on an Incident

1998 ◽  
Vol 1621 (1) ◽  
pp. 36-42 ◽  
Author(s):  
Ronald Koo ◽  
Youngbin Yim
Keyword(s):  
San Francisco ◽  
Travel Behavior ◽  
San Francisco Bay Area ◽  
Traffic Information ◽  
Behavior Changes ◽  
Bay Area ◽  
Telephone System ◽  
Changes Over Time ◽  
The Impact ◽  
Insight Into

How traffic information is obtained and how it affects travel behavior when a major freeway is congested are presented and discussed. Immediately following a major highway incident south of San Francisco that caused congestion, a telephone survey was conducted of commuters who use the affected corridor of the highway. The behavior of commuters before and during their commute at the time of the incident was determined, including obtaining traffic information and how the information influenced changes in route, mode of travel, and departure time. The results of the survey suggest that traveler behavior is largely unaffected by individual incidents of congestion. Furthermore, although a fair proportion of commuters do obtain traffic information, they do not often modify their travel behavior in response. This study is one of several that collectively will provide insight into how travel behavior changes over time and allow the authors to assess the impact of TravInfo Traveler Advisory Telephone System in the San Francisco Bay Area.

Pedestrians, Autonomous Vehicles, and Cities

2016 ◽  
Vol 38 (1) ◽  
pp. 6-12 ◽  
Author(s):  
Adam Millard-Ball
Keyword(s):  
Travel Behavior ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Neighborhoods ◽  
Urban Traffic ◽  
Risk Averse ◽  
Strategic Interactions ◽  
Road Users ◽  
Self Driving Cars ◽  
Time Autonomous

Autonomous vehicles, popularly known as self-driving cars, have the potential to transform travel behavior. However, existing analyses have ignored strategic interactions with other road users. In this article, I use game theory to analyze the interactions between pedestrians and autonomous vehicles, with a focus on yielding at crosswalks. Because autonomous vehicles will be risk-averse, the model suggests that pedestrians will be able to behave with impunity, and autonomous vehicles may facilitate a shift toward pedestrian-oriented urban neighborhoods. At the same time, autonomous vehicle adoption may be hampered by their strategic disadvantage that slows them down in urban traffic.

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