A Google-Map-Based Arterial Traffic Information System

Wu, Yao-Jan; Wang, Yinhai; Qian, Dalin

doi:10.1109/itsc.2007.4357678

Cited by 35 publications

(21 citation statements)

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“…The second set of data is about the traffic network and the transportation infrastructure in the community, as inputs for the traffic model. This set of data is available from local and regional transportation departments and/or web mapping services (e.g., satellite imagery and street view from Google and Bing map) (Farkas et al, ; Wu et al, ). The third set of data is on demography and residents' behaviors on the usage of social media during flood events.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Role of Social Media and Individual Behaviors in Flood Evacuation Processes: An Agent‐Based Modeling Approach

Cai

Sun

et al. 2017

Water Resources Research

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Flood warnings from various information sources are important for individuals to make evacuation decisions during a flood event. In this study, we develop a general opinion dynamics model to simulate how individuals update their flood hazard awareness when exposed to multiple information sources, including global broadcast, social media, and observations of neighbors' actions. The opinion dynamics model is coupled with a traffic model to simulate the evacuation processes of a residential community with a given transportation network. Through various scenarios, we investigate how social media affect the opinion dynamics and evacuation processes. We find that stronger social media can make evacuation processes more sensitive to the change of global broadcast and neighbor observations, and thus, impose larger uncertainty on evacuation rates (i.e., a large range of evacuation rates corresponding to sources of information). For instance, evacuation rates are lower when social media become more influential and individuals have less trust in global broadcast. Stubborn individuals can significantly affect the opinion dynamics and reduce evacuation rates. In addition, evacuation rates respond to the percentage of stubborn agents in a nonlinear manner, i.e., above a threshold, the impact of stubborn agents will be intensified by stronger social media. These results highlight the role of social media in flood evacuation processes and the need to monitor social media so that misinformation can be corrected in a timely manner. The joint impacts of social media, quality of flood warnings, and transportation capacity on evacuation rates are also discussed.

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Section: Discussionmentioning

confidence: 99%

Exploring the Role of Social Media and Individual Behaviors in Flood Evacuation Processes: An Agent‐Based Modeling Approach

Cai

Sun

et al. 2017

Water Resources Research

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“…"Mashup" has become one of the hottest buzzwords in the Web applications area. Many companies, organizations, and institutions are rushing to provide mashup solutions or relabeled existing integration solutions as mashup tools, just like these applications [7,8,[12][13][14][15][16][17].…”

Section: The Information Service For the Public Bicycle Systemmentioning

confidence: 99%

A Geo-Aware and VRP-Based Public Bicycle Redistribution System

Lin

Chou

2012

International Journal of Vehicular Technology

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Public Bicycle System (PBS) has been developed for short-distance transportation as a part of the mass transportation system. The supply and demand of bikes in PBS is usually unbalanced at different stations and needs to be continuously and widely monitored and redistributed. The bicycle redistribution is a part of the vehicle routing problem (VRP). We can apply solutions to the VRP to redistribute bicycle efficiently. However, most solutions to the VRP use the Euclidean distance as the condition factor, which does not take road conditions, traffic regulations, and geographical factors into account, resulting in unnecessary waste of delivery time and human resources. In this work, we propose an actual path distance optimization method for the VRP to adapt the several additional constraints of road problems. We also implement a system that integrates real-time station information, Web GIS, the urban road network, and heuristics algorithms for PBS. The system includes a simulator inside that can assist PBS managers to do the route planning efficiently and find the best scheduling strategy to achieve hotspot analysis and the adjustment of station deployment strategies to reduce PBS operation cost.

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“…This is very important given that some plumes were transient with respect to the measurement transects. Real-time data from Google Maps™ (Wu et al, 2007) aids survey navigation to avoid traffic, a significant consideration in the Los Angeles Basin, allowing alternative route selection to avoid traffic blockages. Additionally, realtime weather from pre-selected weather stations was monitored to provide a regional understanding of evolving meteorological conditions during the survey.…”

Section: Detailed Approachmentioning

confidence: 99%

Validation of mobile in situ measurements of dairy husbandry emissions by fusion of airborne/surface remote sensing with seasonal context from the Chino Dairy Complex

Leifer

Melton

Tratt

et al. 2018

Environmental Pollution

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Mobile in situ concentration and meteorology data were collected for the Chino Dairy Complex in the Los Angeles Basin by AMOG (AutoMObile trace Gas) Surveyor on 25 June 2015 to characterize husbandry emissions in the near and far field in convoy mode with MISTIR (Mobile Infrared Sensor for Tactical Incident Response), a mobile upwards-looking, column remote sensing spectrometer. MISTIR reference flux validated AMOG plume inversions at different information levels including multiple gases, GoogleEarth imagery, and airborne trace gas remote sensing data. Long-term (9-yr.) Infrared Atmospheric Sounding Interferometer satellite data provided spatial and trace gas temporal context. For the Chino dairies, MISTIR-AMOG ammonia (NH) agreement was within 5% (15.7 versus 14.9 Gg yr, respectively) using all information. Methane (CH) emissions were 30 Gg yr for a 45,200 herd size, indicating that Chino emission factors are greater than previously reported. Single dairy inversions were much less successful. AMOG-MISTIR agreement was 57% due to wind heterogeneity from downwind structures in these near-field measurements and emissions unsteadiness. AMOG CH, NH, and CO emissions were 91, 209, and 8200 Mg yr, implying 2480, 1870, and 1720 head using published emission factors. Plumes fingerprinting identified likely sources including manure storage, cowsheds, and a structure with likely natural gas combustion. NH downwind of Chino showed a seasonal variation of a factor of ten, three times larger than literature suggests. Chino husbandry practices and trends in herd size and production were reviewed and unlikely to add seasonality. Higher emission seasonality was proposed as legacy soil emissions, the results of a century of husbandry, supported by airborne remote sensing data showing widespread emissions from neighborhoods that were dairies 15 years prior, and AMOG and MISTIR observations. Seasonal variations provide insights into the implications of global climate change and must be considered when comparing surveys from different seasons.

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A Google-Map-Based Arterial Traffic Information System

Cited by 35 publications

References 0 publications

Exploring the Role of Social Media and Individual Behaviors in Flood Evacuation Processes: An Agent‐Based Modeling Approach

Exploring the Role of Social Media and Individual Behaviors in Flood Evacuation Processes: An Agent‐Based Modeling Approach

A Geo-Aware and VRP-Based Public Bicycle Redistribution System

Validation of mobile in situ measurements of dairy husbandry emissions by fusion of airborne/surface remote sensing with seasonal context from the Chino Dairy Complex

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