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Dr. Thomas Brennan
Civil Engineering, The College of New Jersey, Ewing, NJ 08628, USA

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0 Big Data
0 Infrastructure Management
0 ITS
0 Sustainable Design
0 Transportation

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Journal article
Published: 30 June 2020 in IEEE Internet of Things Journal
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This paper discusses the design and testing of LoRa communication-based IoT devices to track relatively small and in-expensive non-powered assets like concrete roadway barriers within a smart city, highlighting how these requirements differ from those associated with larger and more expensive assets. More specifically, this paper presents an innovative method to minimize LoRa module energy by monitoring for localized movement or ac-celeration as well as changes in received signal strength from the base station. One important aspect of this method is normalizing received signal strength values to account for real-time changes in weather and temperature. This paper also presents a method to optimally place LoRa relays while considering the needs of profes-sionals working in commercial, industrial, and construction set-tings. The authors’ primary objective is to develop, implement, and test hardware with the ability to track these assets effectively while being inexpensive enough to warrant their placement on smaller assets, far-reaching enough to track and communicate with assets over ranges up to 2.5km, durable enough to withstand the stresses of commercial and construction sites, and sustainable enough to operate for 5+ years without manual intervention or an energy source to recharge the unit.

ACS Style

Anthony S. Deese; Joe Jesson; Thomas Brennan; Steven Hollain; Patrick Stefanacci; Emily Driscoll; Connor Dick; Keith Garcia; Ryan Mosher; Brian Rentsch; Andrew Bechtel; Efrain Rodriguez. Long-Term Monitoring of Smart City Assets via Internet of Things and Low-Power Wide-Area Networks. IEEE Internet of Things Journal 2020, 8, 222 -231.

AMA Style

Anthony S. Deese, Joe Jesson, Thomas Brennan, Steven Hollain, Patrick Stefanacci, Emily Driscoll, Connor Dick, Keith Garcia, Ryan Mosher, Brian Rentsch, Andrew Bechtel, Efrain Rodriguez. Long-Term Monitoring of Smart City Assets via Internet of Things and Low-Power Wide-Area Networks. IEEE Internet of Things Journal. 2020; 8 (1):222-231.

Chicago/Turabian Style

Anthony S. Deese; Joe Jesson; Thomas Brennan; Steven Hollain; Patrick Stefanacci; Emily Driscoll; Connor Dick; Keith Garcia; Ryan Mosher; Brian Rentsch; Andrew Bechtel; Efrain Rodriguez. 2020. "Long-Term Monitoring of Smart City Assets via Internet of Things and Low-Power Wide-Area Networks." IEEE Internet of Things Journal 8, no. 1: 222-231.

Articles
Published: 30 April 2019 in Traffic Injury Prevention
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Objective: This article outlines a data collection process that quantifies driver cell phone use using a software-defined radio (SDR) at a signalized intersection. Cell phone use while driving has been shown to be factor that increases the risk of a crash incident. Both operational and enforcement strategies can be applied at locations where high driver cell phone use is identified. Methods: A baseline driver cell phone use observation was made at the intersection, where 9,699 vehicles were observed at the intersection of Carlton Road and State Route 31 (Pennington Road) in Ewing, New Jersey. An SDR cell phone detection device created as part of this study was then deployed at the same intersection to determine whether the SDR device could detect an active cell phone signal. The identification of vehicle cell phone activity using the SDR was conducted a sample of 4,000 vehicles. A visual observation, along with a motion detection camera, was made alongside the SDR to visually confirm cell phones use. Results: Of the 4,000 vehicles sampled using the SDR cell phone detection device, 6.1% of the a.m. peak travel time and 7.6% of the p.m. peak travel time had an active cellular device. A concurrent visual field verification of driver cell phone use showed that approximately 57% (a.m. peak) and 67% (p.m. peak) of the SDR-detected cell phones were visually confirmed to be associated with distracted cell phone use. Conclusions: Once characterized, the frequency of driver cell phone use can be used to justify changes to signal timing protocols. These adjustments could include extending the signal’s “all-red time” or holding “yellow time” longer in order to properly clear the intersection. These data can also be used to identify locations that may require more enforcement measures to dissuade driver cell phone use. Furthermore, the impact of anti–cell phone campaigns or new laws can be quantified by measuring before and after cell phone use in the near term rather than waiting for crash studies at intersections to be completed and analyzed.

ACS Style

Thomas M. Brennan Jr.; Joseph E. Jesson; Pedro Gilberto A. Furlanetto. Quantifying driver cell phone use at signalized intersections using software-defined radio. Traffic Injury Prevention 2019, 20, 359 -364.

AMA Style

Thomas M. Brennan Jr., Joseph E. Jesson, Pedro Gilberto A. Furlanetto. Quantifying driver cell phone use at signalized intersections using software-defined radio. Traffic Injury Prevention. 2019; 20 (4):359-364.

Chicago/Turabian Style

Thomas M. Brennan Jr.; Joseph E. Jesson; Pedro Gilberto A. Furlanetto. 2019. "Quantifying driver cell phone use at signalized intersections using software-defined radio." Traffic Injury Prevention 20, no. 4: 359-364.

Original paper
Published: 05 April 2019 in Journal of Big Data Analytics in Transportation
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Big data from probe vehicles is increasingly becoming an important contributor for determining the regional performance of a transportation roadway network. Recent research has applied aggregated speed data from probe vehicles to quantify travel time variations as a result of recurring congestion, incidents, weather events and other non-recurring congestion. Through the establishment of a base travel time for all roadway segments in a region, any increase in travel time characteristics in the regional networks can be quantified temporally and spatially. This characterization is especially important when determining a region’s congestion resiliency, which is being defined as the ability of a roadway network accommodate failures and return to a baseline congestion after a major capacity reduction to the roadway network. This paper demonstrates how aggregated big data on vehicle speeds obtained from regionally deployed probe vehicles could be used to characterize and visualize the interdependent congestion impacts between regions and across roadway types (interstate, arterial, and local). To demonstrate the models and methodologies, an in-depth analysis of the I-276 Bridge closure incident in Burlington County, NJ near Philadelphia, PA was conducted. The bridge was clzosed after a routine inspected identified a crack in one of the structural members. In total, 90 days of data, which included 90-million speed records, were commercially collected for 1765 roadway segments, was analyzed. A novel performance metric was developed to allow an impact analysis by comparing Burlington County to two adjacent counties, Mercer and Camden. The results showed that the bridge closure did have a definitive, quantifiable impact on the primary road network of the adjacent counties. Subsequent analysis identified specific roadways that were most impacted by the closure. Although this research explores historic speed data, the methodologies presented can be applied to real-time speed data to assist in developing efficient traffic operation plans during major incidents, lane closures and weather events.

ACS Style

Thomas M. Brennan; Ryan A. Gurriell; Andrew J. Bechtel; Mohan M. Venigalla. Visualizing and Evaluating Interdependent Regional Traffic Congestion and System Resiliency, a Case Study Using Big Data from Probe Vehicles. Journal of Big Data Analytics in Transportation 2019, 1, 25 -36.

AMA Style

Thomas M. Brennan, Ryan A. Gurriell, Andrew J. Bechtel, Mohan M. Venigalla. Visualizing and Evaluating Interdependent Regional Traffic Congestion and System Resiliency, a Case Study Using Big Data from Probe Vehicles. Journal of Big Data Analytics in Transportation. 2019; 1 (1):25-36.

Chicago/Turabian Style

Thomas M. Brennan; Ryan A. Gurriell; Andrew J. Bechtel; Mohan M. Venigalla. 2019. "Visualizing and Evaluating Interdependent Regional Traffic Congestion and System Resiliency, a Case Study Using Big Data from Probe Vehicles." Journal of Big Data Analytics in Transportation 1, no. 1: 25-36.

Research article
Published: 11 September 2018 in Transportation Research Record: Journal of the Transportation Research Board
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Probe vehicle speed data has become an important data source for evaluating the congestion performance of highways and arterial roads. Pre-defined spatially located segments known as traffic message channels (TMCs) are linked to commercially available, temporal anonymous probe vehicle speed data. These data have been used to develop agency-wide performance measures to better plan and manage infrastructure assets. Recent research has analyzed individual as well as aggregated TMC links on roadway systems to identify congested areas along spatially defined routes. By understanding the typical congestion of all TMCs in a region as indicated by increased travel times, a broader perspective of the congestion characteristics can be gained. This is especially important when determining the impact of such occurrences in the region as a major crash event, special events, or during extreme conditions such as a natural or human-made disaster. This paper demonstrates how aggregated probe speed data can be used to characterize regional congestion. To demonstrate the methodology, an analysis of vehicle speed data during Hurricane Sandy, the second costliest hurricane in the United States, is used to show the regional impact in 2012. Further, the analysis results are compared and contrasted with comparable periods of increased congestion in 2013, 2014, and 2016. The analysis encompasses 614 TMCs, within 10 miles of the New Jersey coast. Approximately 90 million speed records covering five counties are analyzed in the study.

ACS Style

Jr. Thomas M. Brennan; Mohan M. Venigalla; Ashley Hyde; Anthony LaRegina. Performance Measures for Characterizing Regional Congestion using Aggregated Multi-Year Probe Vehicle Data. Transportation Research Record: Journal of the Transportation Research Board 2018, 2672, 170 -179.

AMA Style

Jr. Thomas M. Brennan, Mohan M. Venigalla, Ashley Hyde, Anthony LaRegina. Performance Measures for Characterizing Regional Congestion using Aggregated Multi-Year Probe Vehicle Data. Transportation Research Record: Journal of the Transportation Research Board. 2018; 2672 (42):170-179.

Chicago/Turabian Style

Jr. Thomas M. Brennan; Mohan M. Venigalla; Ashley Hyde; Anthony LaRegina. 2018. "Performance Measures for Characterizing Regional Congestion using Aggregated Multi-Year Probe Vehicle Data." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 42: 170-179.

Journal article
Published: 01 September 2018 in Infrastructure Asset Management
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ACS Style

Thomas M Brennan; Christopher M Day; Matthew Volovski; Thomas M Brennan Jr.. Ranking statistically similar highway travel corridors using speed datasets within the USA. Infrastructure Asset Management 2018, 5, 96 -104.

AMA Style

Thomas M Brennan, Christopher M Day, Matthew Volovski, Thomas M Brennan Jr.. Ranking statistically similar highway travel corridors using speed datasets within the USA. Infrastructure Asset Management. 2018; 5 (3):96-104.

Chicago/Turabian Style

Thomas M Brennan; Christopher M Day; Matthew Volovski; Thomas M Brennan Jr.. 2018. "Ranking statistically similar highway travel corridors using speed datasets within the USA." Infrastructure Asset Management 5, no. 3: 96-104.

Journal article
Published: 01 September 2018 in Infrastructure Asset Management
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Anonymous probe-vehicle data is being used to evaluate travel time reliability and congestion conditions along roadways. This telematics data is collected from commercial and private cell phones, GPS devices, and on-board vehicles computers. In the last few years, this data has been incorporated into reports of highway and arterial systems to measure congestion conditions. For this paper, the anonymous probe-vehicle data was used to calculate a congestion metric, at bridge locations in New Jersey, based on aggregated yearly congestion hours. The congestion metric was then compared to the functionally obsolete (FO) performance designation defined by the United States (US) National Bridge Inventory (NBI). These FO bridges represent structures who, according to the NBI, theoretically cause congestion. The comparison showed that the functionally obsolete designation did not indicate high levels of congestion and vice versa. Consequently, when the congestion metric was implemented in a rudimentary management strategy, it provided a clearer decision making process over the current traffic carrying metric in the NBI. The results of this study show that the aggregated congestion metric can serve as a useful performance indicator for bridge structures at a regional level, and it can provide value when incorporated into a bridge asset-management program.

ACS Style

Andrew Bechtel; Thomas M Brennan; Kevin Gurski; Jessica Ansley. Using anonymous probe-vehicle data for a performance indicator of bridge service. Infrastructure Asset Management 2018, 5, 85 -95.

AMA Style

Andrew Bechtel, Thomas M Brennan, Kevin Gurski, Jessica Ansley. Using anonymous probe-vehicle data for a performance indicator of bridge service. Infrastructure Asset Management. 2018; 5 (3):85-95.

Chicago/Turabian Style

Andrew Bechtel; Thomas M Brennan; Kevin Gurski; Jessica Ansley. 2018. "Using anonymous probe-vehicle data for a performance indicator of bridge service." Infrastructure Asset Management 5, no. 3: 85-95.

Journal article
Published: 01 November 2016 in Land Use Policy
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Historically the land development process has lacked a decision support structure for evaluating undeveloped parcels of land for compatibility with land use policy and engineering constraints. This paper demonstrates an applied multi-criteria decision support structure for characterizing the spatial distribution and classification of a parcel’s potential to support residential lot construction. This support structure is based on parcel attributes quantified in a typical site feasibility report, to include: potential house yield, wetlands area, soil types, streams (surface drains), and steep slope areas. The analytical capabilities of geographic information system (GIS) are employed in the decision support structure named the constructability assessment method (CAM). CAM integrates a dynamic multi-criteria attribute assessment method, based on the Analytical Hierarchy Process (AHP), for a given set of administrative requirements, and engineering constraints and judgment. The results of a case study using CAM characterized the approximate location of ideal lots for homes construction in an R-1 zoning district located on a 1290 acre land parcel in Loudoun County, VA, while avoiding existing hydric soils, floodplains, steep slopes, and forested areas. The number of ideal lots for a given set of engineering and administrative constraints represented a 65% reduction from the maximum lots permitted by regulatory constraints alone. The methodology used in this case study provides a consistent and repeatable land parcel analysis technique for undeveloped land parcels, and can be adapted and/or extended to a number of similar publicly available geographic datasets and constraint. In estimating optimal development density, CAM meets the needs of zoning administrators as well as the developers, thus offering a demand-driven market-based solution for sustainable land development.

ACS Style

Thomas M. Brennan; Mohan Venigalla. A constructability assessment method (CAM) for sustainable division of land parcels. Land Use Policy 2016, 56, 47 -57.

AMA Style

Thomas M. Brennan, Mohan Venigalla. A constructability assessment method (CAM) for sustainable division of land parcels. Land Use Policy. 2016; 56 ():47-57.

Chicago/Turabian Style

Thomas M. Brennan; Mohan Venigalla. 2016. "A constructability assessment method (CAM) for sustainable division of land parcels." Land Use Policy 56, no. : 47-57.