An evaluation of emerging data collection technologies for travel demand modeling: from research to practice

Lee, Richard J.; Sener, Ipek N.; Mullins, James

doi:10.1080/19427867.2015.1106787

Cited by 43 publications

(28 citation statements)

References 35 publications

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“…Sensors may be classified into two classes. In-vehicle sensors (i.e., GPS on-board, mobile phones) [11,12] allow to obtain space-time coordinates of vehicles along the road network (i.e., FCD). Out-vehicle sensors can be classified into tripwire, able to operate on a road section measuring vehicular flows, speeds, and other variables; and tracking, able to operate on an extended road area and to track vehicle trajectories [13,14].…”

Section: Big Data For Mobilitymentioning

confidence: 99%

Transport System Models and Big Data: Zoning and Graph Building with Traditional Surveys, FCD and GIS

Croce

Musolino

Rindone

et al. 2019

IJGI

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The paper deals with the integration of data provided from traditional transport surveys (small data) with big data, provided from Information and Communication Technology (ICT), in building Transport System Models (TSMs). Big data are used to observe historical mobility patterns and transport facilities and services, but they are not able to assess ex-ante effects of planned interventions and policies. To overcome these limitations, TSMs can be specified, calibrated and validated with small data, but they are expensive to obtain. The paper proposes a procedure to increase the benefits of TSMs’ building in forecasting capabilities, on one side; and limiting the costs connected to traditional surveys thanks to the availability of big data, on the other side. Small data (e.g., census data) are enriched with Floating Car Data (FCD). At the current stage, the procedure focuses on two specific elements of TSMs: zoning and graph building. These processes are both executed considering the estimated values of an intensity function of FCDs, consistently with traditional methods based on small data. The data-fusion of small and big data, operated with a Geographic Information System (GIS) tool, in a real extra-urban context is presented in order to validate the proposed procedure.

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Section: Big Data For Mobilitymentioning

confidence: 99%

Transport System Models and Big Data: Zoning and Graph Building with Traditional Surveys, FCD and GIS

Croce

Musolino

Rindone

et al. 2019

IJGI

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“…Assume that the spatiotemporal instantaneous driving cycles ᵆᵅ , denoting the expected second-by-second speed variations, are available for each road-link (ᵅ, ᵆ) ∈ ᵃ of the network, for all time instants ᵱ ∈ ᵖ . It is worth mentioning that with the current advancements in the Global Positioning System (GPS) devices, it is possible to create a historical archive of such data for the required road-links at different times of a day (Belliss, 2004;Byon, Shalaby, & Abdulhai, 2006;Lee, Sener, & Mullins III, 2016); however, in the event that they are unavailable at the planning stage, they could be instead generated synthetically using the approach proposed later in the paper.…”

Section: The Instantaneous Fuel Consumption Estimation Modelmentioning

confidence: 99%

The multi-objective Steiner pollution-routing problem on congested urban road networks

Raeesi

Zografos

2019

Transportation Research Part B: Methodological

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This paper introduces the Steiner Pollution-Routing Problem (SPRP) as a realistic variant of the PRP that can take into account the real operating conditions of urban freight distribution. The SPRP is a multi-objective, time and load dependent, fleet size and mix PRP, with time windows, flexible departure times, and multi-trips on congested urban road networks, that aims at minimising three objective functions pertaining to (i) vehicle hiring cost, (ii) total amount of fuel consumed, and (iii) total makespan (duration) of the routes. The paper focuses on a key complication arising from emissions minimisation in a time and load dependent setting, corresponding to the identification of the full set of the eligible road-paths between consecutive truck visits a priori, and to tackle the issue proposes new combinatorial results leading to the development of an exact Path Elimination Procedure (PEP). A PEP-based mixed integer programming model is further developed for the SPRP and embedded within an efficient mathematical programming technique to generate the full set of the non-dominated points on the Pareto frontier of the SPRP. The proposed model considers truck instantaneous Acceleration/Deceleration (A/D) rates in the fuel consumption estimation, and to address the possible lack of such data at the planning stage, a new model for the construction of reliable synthetic spatiotemporal driving cycles from available macroscopic traffic speed data is introduced. Several analyses are conducted to demonstrate the added value of the proposed approach, exhibit the trade-off between the business and environmental objectives on the Pareto front of the SPRP, show the benefits of using multiple trips, and verify the reliability of the proposed model for the generation of driving cycles. A real road network based on the Chicago's arterial streets is also used for further experimentation with the proposed PEP algorithm.

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“…Among these components, GPS data are applied more extensively due to their finer spatial and temporal resolution, which can be exploited to yield high quality route and travel time data by transportation researchers, planners, and modellers. In practice, due to the low penetration rates of these devices among transportation networks' users, excessive costs and the probability of statistical bias for specific social groups such as young people or business people (Bauer et al 2018), most applications of GPS data are going through developing process as to modelling components, calibration and validation (Lee et al 2016). The CDRs are the recent attempts made in improving travel surveys.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Sensor Location and Origin–destination Matrix Observation With and Without Sensors on Uncongested Networks

2019

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The Origin–Destination (O–D) matrix, is an important information in transportation planning and traffic control. Rapid changes in land use, particularly in developing countries, have been and are on an increase, which makes the estimation and observation of this matrix more significant. The objective of this paper is to observe O–D matrix under two scenarios. In the first scenario, it is assumed that the traffic network is equipped with path-ID sensors. In this situation, the goal is to determine the optimal number and location of these sensors in the network, where by applying collected information through these sensors, the O–D matrix is observed. Because path-ID sensors are not available in many cities, in the second scenario the interview alternative is proposed in order to observe O–D matrix. The interview method has encountered some restrictions. Several mathematical programming models have been developed to overcome these restrictions. To illustrate these proposed methodologies, they are applied in the Nguyen–Dupuis transportation network and the results are analysed. By applying the model on the intercity road network in the Province of Isfahan (Iran), a large network, the efficiency of these proposed models is demonstrated. Finally, some conclusions and final recommendations are included.

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An evaluation of emerging data collection technologies for travel demand modeling: from research to practice

Cited by 43 publications

References 35 publications

Transport System Models and Big Data: Zoning and Graph Building with Traditional Surveys, FCD and GIS

Transport System Models and Big Data: Zoning and Graph Building with Traditional Surveys, FCD and GIS

The multi-objective Steiner pollution-routing problem on congested urban road networks

Optimal Sensor Location and Origin–destination Matrix Observation With and Without Sensors on Uncongested Networks

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