Link-based measurement model to estimate route choice parameters in urban pedestrian networks

Oyama, Yuki; Hato, Eiji

doi:10.1016/j.trc.2018.05.013

Cited by 34 publications

(9 citation statements)

References 31 publications

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“…A recent study conducted with the GPS data of pedestrians proposed a method to simultaneously estimate the path choice model parameters for a pedestrian during estimating the various errors for each link [28]. However, the study assumed that the pedestrian networks were known.…”

Section: Spatial Representation Of Gps Datamentioning

confidence: 99%

“…The widespread use of Global Positioning System (GPS) devices has enabled the collection of large amounts of tracking data, which has in turn allowed data analysis by several approaches, including understanding the travel behaviors of people [24,25] and improving mobility services [26]. This is no exception in the case of analyzing pedestrians [27,28]. Typically, pedestrian networks are not as readily available as road networks, which is another major problem in handling pedestrian activity [27].…”

Section: Introductionmentioning

confidence: 99%

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Activity Scheduling Behavior of the Visitors to an Outdoor Recreational Facility Using GPS Data

Hidaka

Yamamoto

2021

Sustainability

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Understanding the decision-making behavior of pedestrians is essential for urban designers and developers in enhancing the commercial and aesthetic value of streets and other urban spaces. However, limited research has been conducted on the activity scheduling behavior of pedestrians. The majority of the studies conducted on outdoor facilities utilize spatial representations by links and are unable to sufficiently represent the highly flexible behavior of pedestrians. This study proposes a new method to discretize data from the global positioning system (GPS) into a two-dimensional grid-based spatial representation with a high spatial resolution. The information regarding the stay at the point of interests (POIs) is extracted from the discretized data, and the activity scheduling model is estimated. The estimation results indicate that the visitors’ attributes, such as the age of the representative and number of children, affect the probability of the activity choice and the time spent at the POI. The probability of choosing the main gate increases in the latter half of the stay, confirming the existence of time pressure. The information on the decision-making behavior of the visitors to a facility, obtained from the GPS data, can be applied to the data-oriented spatial design process to create attractive and lively spaces.

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Section: Spatial Representation Of Gps Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Activity Scheduling Behavior of the Visitors to an Outdoor Recreational Facility Using GPS Data

Hidaka

Yamamoto

2021

Sustainability

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“…MM methods do the job of locating a moving car on a road utilizing map data in digital form with GPS receiver knowledge. MM implementations serve many functions such as travel time estimation (Tang et al, 2018;Rahmani et al, 2017), route selection parameter estimation (Oyama and Hato, 2018), analysis of gridlock network (Oyama and Hato, 2017), and motion anomaly identification (Qin et al, 2019). In India, we claim our cities are being transformed into a smart cities; therefore, we are in the need of a major GIS network to turn a simple city into a smart one.…”

Section: Related Workmentioning

confidence: 99%

Prediction and Anticipation Features-Based Intellectual Assistant in Location-Based Services

Esmaeili

Gupta

Başçı

et al. 2021

International Journal of System Dynamics Applications

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Intellectual assistant in a location based service (LBS) is considered here as assistive technology in healthcare system for helping users with sensory, physical or intellectual diminished capabilities. The proposed system has the speculative computation module which helps in orientation for predicting the users’ error and alerting them for ensuring a true traveling direction well in advance for the handicapped people of mild to moderate condition of disability in earlier phases of cognitive disabilities. A person with cognitive impairments has the Trajectory (T) associated with them which is a sequenced set of past global positioning system (GPS) points. The implementation and evaluation of the trajectory data mining method have been done for the purpose of achieving the travel route to the user preferences. For the simulation of proposed system, a client server queuing model has been used. The results show the minimization of the overhead with better precision as compared to earlier existing path guiding systems.

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“…Also, the dispersion-constrained model in [3] is based on a maximum-entropy approach similar to the RSP framework, and involves the estimation of a temperature parameter. Other works, such as [15,36,37,[46][47][48] focus, in various trafficrelated settings, on the problem of estimating parameters on edges or parameters associated to features on edges, which in some cases corresponds to the problem of estimating the inverse temperature and in some case to the problem of estimating edge costs in the RSP framework (the latter of which is addressed only briefly in the current work, in Section 2.4). The above cited works in logit assignment and related models is in many respects very close and similar to the RSP model.…”

Section: Related Workmentioning

confidence: 99%

Maximum likelihood estimation for randomized shortest paths with trajectory data

Kivimäki

Moorter

Panzacchi

et al. 2020

Journal of Complex Networks

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Randomized shortest paths (RSPs) are tool developed in recent years for different graph and network analysis applications, such as modelling movement or flow in networks. In essence, the RSP framework considers the temperature-dependent Gibbs–Boltzmann distribution over paths in the network. At low temperatures, the distribution focuses solely on the shortest or least-cost paths, while with increasing temperature, the distribution spreads over random walks on the network. Many relevant quantities can be computed conveniently from this distribution, and these often generalize traditional network measures in a sensible way. However, when modelling real phenomena with RSPs, one needs a principled way of estimating the parameters from data. In this work, we develop methods for computing the maximum likelihood estimate of the model parameters, with focus on the temperature parameter, when modelling phenomena based on movement, flow or spreading processes. We test the validity of the derived methods with trajectories generated on artificial networks as well as with real data on the movement of wild reindeer in a geographic landscape, used for estimating the degree of randomness in the movement of the animals. These examples demonstrate the attractiveness of the RSP framework as a generic model to be used in diverse applications.

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Link-based measurement model to estimate route choice parameters in urban pedestrian networks

Cited by 34 publications

References 31 publications

Activity Scheduling Behavior of the Visitors to an Outdoor Recreational Facility Using GPS Data

Activity Scheduling Behavior of the Visitors to an Outdoor Recreational Facility Using GPS Data

Prediction and Anticipation Features-Based Intellectual Assistant in Location-Based Services

Maximum likelihood estimation for randomized shortest paths with trajectory data

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