This project proposes a smart permitting system to address those pressing issues related to permitting. First, the permitting system consists of a social-costs-based pricing system to internalize the externalities attributed to a specific right-of-way permit. A network mobility model is adopted to quantify the impact of a permit on specific roads, times of day, and days of week. We will further develop a GIS-based web application to visualize the impacts and query the fee of a right-of-way permit in real-time. Second, we will develop a data-driven permit supervision system that supervises and enforces the right-of-way permits through multi-source datasets, including traffic speeds, traffic volumes, and social media data. We will also develop a web application to visualize the information we obtained from the real-time data, alert locations that are likely to violate permits, and recommend routes and locations to inspectors for visual inspection and enforcement. In this project, we will develop a prototype of the smart permitting system, validate the system together with the DOMI team, and pilot the system in a small area in the city and Cranberry Township.
Smart Right-of-Way Permitting System for the City of Pittsburgh: coordination, pricing and enforcement
Public roads, streets, and sidewalks are managed by the City of Pittsburgh and those public space are used by all residents. The Department of Mobility and Infrastructure (DOMI) is responsible for issuing and enforcing permits for occupancy of the public right-of-way in the City of Pittsburgh, such as for construction projects and utility line repair and installation. A contractor must obtain the necessary permits from DOMI before obstructing or performing any construction activities within the public space.
Presently, DOMI charges limited administrative fees for each right-of-way permit, regardless of the work’s impact on the social welfare. The impact of a right-of-way permit includes, but is not limited to, increasing the congestion level, hampering the accessibility of points of interest in the city, and reducing the availability of sidewalks for pedestrians (Hague, 2015). In fact, the impact of each right-of-way permit can vary significantly in terms of its location, time, and duration. From economics perspectives, these impacts can be viewed as the negative externality brought by a right-of-way permit (Suntory and Disciplines, 2019). Hence the permittees should be responsible for the externality by paying a corresponding fee serving the social welfare (Small, 1992). In the current flat-fee permitting system, the negative externality is not properly charged to the permittees. Therefore, current permitting system is not efficient enough to achieve optimal social welfare. As a result, it encourages suboptimal usage of public space to some extent.
Another issue in the permitting system is the lack of resources for supervising and enforcing the right-of-way permits. Permittees may obstruct the traffic for longer time than requested, or they may block the whole road instead of using one lane as per the requirements in the permit. Currently, the enforcement of the right-of-way permit is conducted by visual check of trained inspectors, hence it requires enormous human and equipment resources.
In 2018, there are in total 17,575 right-of-way permits issued by DOMI. The intensive use of public rights-of-way permits may cause tremendous social externalities and require a great number of resources for coordination, supervision and enforcement. Therefore, it is in great need for the city to build a smart right-of-way permitting system that: 1) properly evaluates the social impact of a right-of-way permit, with proper pricing to ensure social equity and social optimum; 2) intelligently supervises and enforces the permits given very limited human resources.
In view of this, this project proposes a smart permitting system to address those pressing issues related to permitting. First, the permitting system consists of a social-costs-based pricing system to internalize the externalities attributed to a specific right-of-way permit. A network mobility model is adopted to quantify the impact of a permit on specific roads, times of day, and days of week (Qian and Zhang, 2013; Qian and Rajagopal, 2014). We will further develop a GIS-based web application to visualize the impacts and query the fee of a right-of-way permit in real-time. Second, we will develop a data-driven permit supervision system that supervises and enforces the right-of-way permits through multi-source datasets, including traffic speeds, traffic volumes, and social media data. We will also develop a web application to visualize the information we obtained from the real-time data, alert locations that are likely to violate permits, and recommend routes and locations to inspectors for visual inspection and enforcement. In this project, we will develop a prototype of the smart permitting system, validate the system together with the DOMI team, and pilot the system in a small area in the city and Cranberry Township.
In this project, we will build a smart right-of-way management system for the city of Pittsburgh which compliments its permit issuance process. The system consists of two major components: 1) an impact-based permit pricing system; 2) a data-driven permit supervision system. In particular, this project solves the following problems for the city of Pittsburgh:
• How does a right-of-way permit impact the social welfare, including traffic congestion, accessibility in cars and buses, and sidewalk availability?
• How to charge the right-of-way permits according to their respective social consequences?
• How to supervise the enforcement of the right-of-way permits given limited human resources?
Task 1: Data preparation
The first task focuses on the data collection, data cleaning and data processing for the proposed permitting system. Multi-source data will be combined and used for evaluating the impact of right-of-way permits in the city of Pittsburgh. Four types of data will be considered in this project: 1) GIS map data for understanding the network topology and road properties; 2) traffic data for analyzing the impact traffic obstruction or street openings; 3) other data sources for categorizing the traffic impacts under different circumstances; and 4) permits data from DOMI. Details of the datasets are presented as follows.
• Roads centerlines: road centerlines data is obtained from the City of Pittsburgh in the format of shapefile. The dataset includes the information of each road owned and managed by Allegany county, such as the number of lanes, speed limit, road capacity, road level.
• Point of interests (POI) data: the POI data can be obtained from Google Maps, and it contains the locations and basic information of all restaurants, grocery stores, markets, parks and other important locations. POI data will be used to evaluate the accessibility issue of a right-of-way permit.
• Travel time: the travel time (or traffic speed) data is obtained from INRIX and HERE, and it contains the real-time travel time for all highways and major roads. Travel time data can be used to understand the congestion level and measure the accessibility.
• Traffic volume: the traffic volume data is obtained from the City of Pittsburgh and PennDOT, and it contains the traffic counts for a selected number of roads every 15 minutes. The traffic volume data can be used to understand the traffic demand of the roads.
• Transit/Pedestrian/Cyclists data: the pedestrian/Cyclists data can be obtained from Port Authority of Allegheny County, the City of Pittsburgh and Make My Trip Counts survey, and it contains the pedestrian counts in downtown Pittsburgh. In addition, shared bicycle usage data can be obtained from HealthyRide. Those data can be potentially used to quantify the impact of right-of-way permits on pedestrians.
• Permit data: details of permits (time, location, duration, enforcement etc.) issued in the past few years.
• List of events, holidays and existing construction plans: the data can be obtained from Pittsburgh City Paper, and it is used to identify different traffic patterns and categorize the traffic impact of right-of-way permits. For example, the traffic obstruction around a football game will significantly increase the congestion and induce higher social costs.
• Social media data: the social media data include the traffic, right-of-way related complaints from Twitter and Waze. The data can be potentially used to validate if the permittees obey the rules and finish their work on time.
We have already acquired and archived some of the data sets listed above at the Mobility Data Analytics Center (MAC) directed by PI Sean Qian. Each of the data sets will be cleaned, processed and archived in a specific format for further analysis. Basic visualization and exploration will be conducted to validate and understand the data.
• Datasets ready for future research
• Data guide on data exploration efforts
Task 2: A social-costs-based permit pricing system
Task 2 focuses on developing an impact-based pricing system using the regional mobility model. The mobility model evaluates the impact of a certain right-of-way permit in the traffic networks, and it builds a comprehensive model to describe the relationship of the traffic demand, travelers’ route choices and network conditions coherently. The model evaluates the traffic network conditions under different traffic demand and network configurations, and the network configurations can include various right-of-way permits such as closing one lane, closing the whole road segment and work zones on the roads. The Mobility Data Analytics Center has developed state-of-art regional mobility model for Pittsburgh metropolitan area and the model will be carefully calibrated using the data collected in Task 1.
To evaluate the impact of a certain permit, we run the mobility model before and after the traffic obstruction or street openings, and the impact is the difference between the two scenarios. We evaluate the impact of a certain permit from four perspectives: traffic congestion, revenue, accessibility and sidewalk availability.
• Traffic congestion: By running the mobility model, we can evaluate the additional traffic congestion induced by the right-of-way permits. The induced congestion can be viewed as traffic impacts of a certain permit.
• Accessibility: By running the mobility model and checking with the POI data, we can understand if a certain permit hampers the accessibility to a good, service, activity or destination (Litman, 2003). Equity issue will also be considered at the same time by evaluating the accessibility in different residential areas.
• Sidewalk availability: Some right-of-way permits may require using the sidewalk or curbs. We will evaluate the impact on the pedestrians if the sidewalk is closed by using the curb data and pedestrian traffic study data.
• Parking revenue: we will evaluate the potential loss of on-street parking revenue if the permit is associated with parking lane closure.
Through a comprehensive evaluation using the mobility model, we can compute the total impact of a certain right-of-way permit. The impact can be viewed as the externality caused by the permit, and hence the city can internalize the externality by charging the permittee and thereby achieve the social optimal. We further develop a pricing system to compute a fee the permittees need to pay for a certain right-of-way permit.
The proposed pricing system considers the fee of a certain permit under various factors, such as time of day, day of week, events, holidays, etc. Generally, a traffic obstruction permit on a major road, during peak hours and lasts long will imply a higher fee, while a traffic obstruction permit on a minor road, during non-peak hours and uses less time will imply a lower fee. More factors can be further considered upon the discussion with Department of Mobility & Infrastructure (DOMI) in the City of Pittsburgh.
We will develop a web application that visualizes the importance of each road and the impact of each right-of-way permit. The web application will also provide the suggested fee for each permit and the users can query the fees on the web application.
• Report on the modeling efforts and factor analysis
• A web-application to visualize the impact, query the fee of a right-of-way permit
Task 3: Data-driven permit supervision system
Task 3 aims at building a permit supervision system to enforce the permittees to obey the permits they requested and finish their work on time. We adopt a data-driven approach to supervise the enforcement of permits. We formulate the supervision problem as an anomaly detection problem, and the multi-source data will be used to identify the non-recurrent traffic patterns. By comparing the permits with the non-recurrent traffic patterns, we can infer if the permittees obey the permits and finish their work on time. The social media data can also be used to identify the violation of permits. For example, if a traffic obstruction project is supposed to end yesterday, but we still see many tweets complaining about the obstruction today, then we know the permittees did not finish their work on time.
In this task, we will develop a real-time supervision system that scraps traffic data from INRIX as well as social media data from Twitter and Waze. The real-time data will be used to identify the non-recurrent traffic patterns and right-of-way related complaints, and these traffic patterns and complaints will be matched with the existing right-of-way permits to validate if the permit is correctly enforced. The supervision system will provide the most suspicious locations for DOMI to conduct a visual check and on-site enforcement.
We will also develop a web application to visualize the information we obtained from the real-time data. The application will also suggest the most suspicious locations where the permits might be violated, DOMI can send people for visual check.
• Report on the details of the supervision system.
• A web-application to visualize non-recurrent traffic patterns and right-of-way related complaints. It also lists the most suspicious locations where the permits might be violated.
Task 4: System validation and field tests
Using the pricing and supervision systems developed in Task 2 and Task 3, Task 4 conducts a case study in the city of Pittsburgh. The task is two-fold: 1) validating the correctness and effectiveness of the proposed systems; 2) conducting field tests in real-world environments.
We will work closely with the city, especially the DOMI to check if the permit fee computed by the new permitting system makes sense. We will also compute the differences between the new permit fees with the original fees and interpret the reasons.
After the careful validation, we will work with the city to test the proposed systems in a small area. We measure the effects of the systems by evaluating the congestion level, accessibility and sidewalk availability. We may need to interview the permittees to understand their behaviors before and after the new permitting system.
• Report on the system validation and field test results
• A brochure for the executive summary of the smart permitting system
Hague, D.T., 2015. Evaluation of Urban Work Zones: Impacts on Businesses, Pedestrians, and Interchanges.
Litman, T., 2003. Measuring transportation: traffic, mobility and accessibility. ITE J. 28–32.
Qian, Z. (Sean), Michael Zhang, H., 2013. Full Closure or Partial Closure? Evaluation of Construction Plans for the I-5 Closure in Downtown Sacramento. J. Transp. Eng. 139, 273–286. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000502
Qian, Z., Rajagopal, R., 2014. Optimal dynamic parking pricing for morning commute considering expected cruising time. Transp. Res. Part C Emerg. Technol. 48, 468–490. https://doi.org/10.1016/j.trc.2014.08.020
Small, K.A., 1992. Using the revenues from congestion pricing. Transportation (Amst). 19, 359–381. https://doi.org/10.1007/BF01098639
Suntory, T., Disciplines, R., 2019. Externality 29, 371–384.
Task Description Month Due from Notice to Proceed Date
1 Data preparation 1
2 Permit pricing system 6
3 Data-driven permit supervision system 10
4 Field test and final report 12
We will develop a prototype of web-based the smart permitting system, validate the system together with the team of DOMI and Cranberry Township, and pilot the system in a small area in the city and Cranberry Township.
Expected Accomplishments and Metrics
The anticipated outcomes are 1) file a patent for this permitting system concept; 2) a web-based application to visualize and understand permit impacts; and 3) increase revenue related to right-of-way permits by 50%; 4) reduce traffic congestion and improve transportation accessibility.
||Faculty - Untenured, Tenure Track
Amount of UTC Funds Awarded
Total Project Budget (from all funding sources)
|Data Management Plan
||Dec. 10, 2019, 10:50 a.m.
||March 9, 2020, 7:49 a.m.
||Smart Right-of-Way Permitting System for the City of Pittsburgh: coordination, pricing and enforcement
||March 14, 2021, 7:12 p.m.
||Sept. 18, 2020, 2:35 p.m.
||Smart Right-of-Way Permitting System for Cranberry Township: coordination, pricing and enforcement
||March 14, 2021, 7:12 p.m.
||March 14, 2021, 7:12 p.m.
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||Deployment Partner Deployment Partner