Researchers on travel behavior and regional economic trends increasingly rely on multiple data sources to locate employers and site-specific employment. In a previous study, we proposed a method to ...assess and integrate multiple sources of employment data using three components: the Google Places application programming interface (API), a business existence verification model, and manual reviews of sampled data. This paper updates our previous methodology with a dual conditional classification of incoming and previously verified employment data made possible by checks using Google Places API and two rounds of string comparisons for both business names and establishment locations. The resulting match classes distinguish well-matched or confirmed business listings from those that require additional review to evaluate potential business closure or relocation. This screening process, augmented with fuzzy logic string matching techniques, reduces the effort needed to update employer information and assists with automated data standardization and deduplication, integrating incoming employment information with a database of verified employers.
Current and accurate site-specific employment data is a critical but vexing element in research on travel behavior patterns and trends in regional economic development. Typically, no single source of ...data provides comprehensive and reliable coverage for a regional metropolitan area. Developing multiple employment data sources is cumbersome and time-consuming, but a procedural approach can streamline data preparation and management. This study proposes a method to assess and integrate multiple sources of employment data using Google Places API and a business existence verification model. Other techniques, including data deduplication and prioritization, address standardization, and missing employer discovery, are addressed as means to improve data accuracy and coverage.
In this paper, we propose a new network representation for modeling schedule-based transit systems. The proposed network representation, called trip-based, uses transit vehicle trips as network edges ...and takes into account the transfer stop hierarchy in transit networks. Based on the trip-based network, we propose a set of path algorithms for schedule-based transit networks, including algorithms for the shortest path, a logit-based hyperpath, and a transit A*. The algorithms are applied to a large-scale transit network and shown to have better computational performance compared to the existing labeling algorithms.
Accurate turning movement counts (TMCs) data collected from regional-wide signalized intersections is critical to regional transportation planning and simulation modeling. A variety of existing ...traffic sensors, configured at intersections for traffic detection and signal control, can generate a large amount of real-time high-resolution event-based data from traffic controllers but few of these sensors are configured to collect TMC. This paper proposes a methodology for estimating network-level TMC using existing traffic controller event-based data without installing additional sensors. First, relevant features that can indicate traffic arrival are extracted from existing event-based data, including detector occupancy time, detector-triggered count, and green time duration. With these features, a multi-output multilayer neural network model is developed to estimate TMC. To further improve network-level TMC estimation accuracy, intersection infrastructure data and point-of-interest (POI) data are included as exogenous variables for the proposed model. Ninety-three signalized intersections are chosen from the Pima County region, Arizona, to calibrate and verify the developed model. The validation results show that the proposed model can accurately estimate TMC, as indicated by a median Root Mean Square Error (RMSE) of 41 veh/15 min, 11 veh/15 min, and 12 veh/15 min for through movement, left-turn movement, and right-turn movement volume estimation, respectively. This research provides a new possibility of utilizing existing data sources to obtain network-level TMC data without additional infrastructure and labor costs for transportation agencies.
As fare and data collection technology has developed, the resolution of collected data has reached the level of the individual traveler in investigations of transit passenger behavior. This paper ...investigates the use of these data to estimate passenger origins and destinations at the level of individual stops. Because of a lack of information from the fare collection system, researchers still need some estimate of passengers' alighting stops to complete each passenger trip chain on a specific day. Automated fare collection (AFC) and automated vehicle location (AVL) systems are the inputs to the estimation. Instead of typical AVL data, the paper proposes two models to estimate the alighting stop; both consider passenger trip chaining by using AFC data, transit schedule data (Google's General Transit Feed Specification), and automated passenger counter (APC) data. The paper validates the model by comparing the output to APC data with vehicle location data (APC-VL) and performs sensitivity analyses on several parameters in the models. To detect transfer trips, the new models propose a submodel that takes into account the effect of service headway in addition to some typical transfer time thresholds. Another contribution of this study is the relative relaxation of the search in finding the boarding stops, which enables the alternative algorithm to detect and fix possible errors in identification of the boarding stop for a transaction. As a result, the paper provides algorithms for the proposed models and sensitivity analysis for several predefined scenarios. The results are based on data and observed bus passenger behavior in the Minneapolis–Saint Paul, Minnesota, area.
A simple but efficient algorithm is proposed for finding the optimal path in an intermodal urban transportation network. The network is a general transportation network with multiple modes (auto, ...bus, rail, walk, etc.) divided into the two major categories of private and public, with proper transfer constraints. The goal was to find the optimal path according to the generalized cost, including private-side travel cost, public-side travel cost, and transfer cost. A detailed network model of transfers between modes was used to improve the accounting of travel times during these transfers. The intermodal path algorithm was a sequential application of specific cases of transit and auto shortest paths and resulted in the optimal intermodal path, with the optimal park-and-ride location for transferring from private to public modes. The computational complexity of the algorithm was shown to be a significant improvement over existing algorithms. The algorithm was applied to a real network within a dynamic traffic and transit assignment procedure and integrated with a sequential activity choice model.
The concept of a hyperpath was introduced for handling passenger strategies in route choice behavior for public transit, especially in a frequency-based transit service environment. This model for ...handling route choice behavior has been widely used for planning transit services, and hyperpaths are now applied in areas beyond public transit. A hyperpath representing more specific passenger behaviors on a network based on transit schedules is proposed. A link-based time-expanded (LBTE) network for transit schedules is introduced; in the network each link represents a scheduled vehicle trip (or trip segment) with departure time and travel time (or arrival time) between two consecutive stops. The proposed LBTE network reduces the effort to build a network based on transit schedules because the network is expanded with scheduled links. A link-based representation of a hypergraph with existing hyperpath model properties that is directly integrated with the LBTE network is also proposed. Transit passenger behavior was incorporated for transfers in the link-based hyperpath. The efficiency of the proposed hyperpath model was demonstrated. The proposed models were applied on a test network and a real transit network represented by the general specification of Google's transit feed.
This paper presents an efficient algorithm that finds the intermodal optimal tour (origin to origin) in a time-dependent transportation network while the algorithm implicitly solves the park-and-ride ...facility choice problem with the inherent park-and-ride constraints for a traveler with a sequence of destinations to visit. To solve the problem, a network expansion technique that captures the constraints of park-and-ride behavior in the model and that transforms the park-and-ride choice problem into a dynamic network flow problem is introduced. An efficient iterative labeling algorithm that finds the optimal intermodal tour to serve the sequence of activities is also introduced. Multisource shortest-path runs are used in the iterative labeling algorithm to find the optimal tour with several intermediate destinations in an efficient manner. The performance of the algorithm is compared with the performance of existing approaches, and improvement is indicated. The solution method proposed benefits from the advantages of Dijkstra's shortest-path algorithm, which is made possible by (a) a nontrivial transformation of the original problem into a dynamic network flow problem and (b) an innovative use of a multisource shortest path in the context of origin–destination choice. The solution algorithm integrates time-dependent auto and transit shortest-path algorithms to find the optimal tour. The algorithm is implemented, coded, and tested on a real network, and the results are promising.
The development of integrated land use–transport model systems has long been of interest because of the complex interrelationships between land use, transport demand, and network supply. This paper ...describes the design and prototype implementation of an integrated model system that involves the microsimulation of location choices in the land use domain, activity–travel choices in the travel demand domain, and individual vehicles on networks in the network supply modeling domain. Although many previous applications of integrated transport demand–supply models have relied on a sequential coupling of the models, the system presented in this paper involves a dynamic integration of the activity–travel demand model and the dynamic traffic assignment and simulation model with appropriate feedback to the land use model system. The system has been fully implemented, and initial results of model system runs in a case study test application suggest that the proposed model design provides a robust behavioral framework for simulation of human activity–travel behavior in space, time, and networks. The paper provides a detailed description of the design, together with results from initial test runs.
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