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P. Eng – One of the best experts on this subject based on the ideXlab platform.
Park-and-Ride Access Station Choice Model for Cross-Regional Commuter Trips in the Greater Toronto and Hamilton Area (GTHA)Transportation Research Board 93rd Annual Meeting, 2014Co-Authors: Mohamed Salah Mahmoud, Khandker Nurul Habib, P. EngAbstract:
26 27 28 29 30 31 32 33 34 35 36 37 38 Number of Words (Maximum 7,500): 5,735 (words) + 2 (Tables)*250 + 5 (Figures)*250 = 7,485 39 40 41 42 43 44 45 46 Abstract 1 The paper presents an investigation on park-and-ride (P&R) access station choices of cross-2 regional commuter in the Greater Toronto and Hamilton Area (GTHA). Data from a household 3 travel survey conducted in 2006 in the GTHA is used for empirical investigation. The household 4 travel survey data is supplemented by data from transit service operators regarding P&R station 5 locations, parking lot capacities, parking costs, surrounding land use, and station amenities. 6 Three groups of park-and-ride users are defined herein: individuals who have only local transit 7 (TTC Subway) stations within a reasonable reach, individuals with only regional transit (GO 8 Rail) stations within reach, and individuals who have both GO Rail and TTC Subway stations 9 within reach. Different model structures and specifications are tested and three discrete choice 10 models are estimated. Empirical models reveal that access distance and the relative station 11 direction (toward the work place) are the primary factors affecting transit station choice for park-12 and-ride options. However, between station distance and relative station direction, commuters 13 are more sensitive to changes in station access distance than to changes in the relative station 14 direction from their households. In addition, the empirical models reveal that local transit park-15 and-ride users are less sensitive to access distance than regional transit park-and-ride users. The 16 results of this investigation can be useful in future transit station design projects in order to 17 attract more commuters to use park-and-ride.
Bart Van Arem – One of the best experts on this subject based on the ideXlab platform.
Stated Choice Experiment on Mode Choice in an Era of Free-Floating Carsharing and Shared Autonomous Vehicles: Raw DataTransportation Research Board 96th Annual Meeting, 2017Co-Authors: Konstanze Winter, Oded Cats, Karel Martens, Bart Van AremAbstract:
Phone number: +31 15 2786342 39 Fax number: +31 15 2787956 40 Email: firstname.lastname@example.org 41 42 Word Count: 6139 + 4 Tables (1000) + 1 Figure (250) = 7389 words 43 44 45 Submission Date: July 2016 46 Winter, Cats, Martens, van Arem 2 2 ABSTRACT 1 New forms of shared mobility such as Free-Floating Carsharing services and Shared 2 Autonomous Vehicles have the potential to change urban travel behaviour. In this paper a 3 stated choice experiment on mode choice among a sample of the Dutch urban population is 4 presented, in which the particular features of free-floating carsharing and shared autonomous 5 vehicles in comparison to private vehicles and public transportation are examined. The most 6 explanatory and robust mode choice models were obtained by estimating nested logit models 7 with two categories capturing vehicle automation or vehicle ownership, and a nested logit 8 model with three categories capturing who is performing the driving task (the commuter, a 9 human driver or an autonomous vehicle). Interpreted as mode preference, the alternative-10 specific constants of the utility functions reveal a strong impact of vehicle automation on 11 mode choice: while early adopters of mobility trends show a clear preference for shared 12 autonomous vehicles over all other modes, normal and late adopters show a clear aversion 13 towards this mode. In terms of vehicle sharing, no preference of sequentially shared modes 14 over a simultaneously shared bus could be determined. Participants currently not having 15 access to carsharing services show a stronger preference towards free-floating carsharing than 16 the early adopters subscribed to carsharing.
D Nguyen – One of the best experts on this subject based on the ideXlab platform.
Title: Improvement of Borehole Thermal Energy Storage Design Based on Experimental and Modelling ResultsEnergy & Buildings, 2014Co-Authors: Simone Lanini, F. Delaleux, Xavier Py, R Oli, Regis Olives, D NguyenAbstract:
11 Underground Thermal Energy Storage appears to be an attractive solution for solar 12 thermal energy storage . The SOLARGEOTHERM research project aimed to evaluate 13 the energetic potential of borehole thermal energy storage by means of a full – scale 14 experimental device and heat transfer models . Analysis of the experimental data 15 showed that a single borehole is not efficient for storage . Application of a 1D 16 analytical model showed that the heat transfer fluid in the geothermal probe lost 17 15 per cent of its energy at a depth of 100 m and 25 per cent at 150 m . A 3D 18 multilayer numerical model was then developed and validated against the 19 experimental data . This model was then used to simulate different configurations 20 over many years . Lastly , a theoretical approach to optimising design of a borehole 21 thermal energy store (BTES) was proposed . A relation was established that enables 22 comparison of the storage characteristic time of any vertical BTES to an optimum 23 one . Based on these experimental , modelling and theoretical results , guidelines are 24 formulated to optimise the design of vertical borehole fields with an objective of inter – seasonal heat storage . In particular , borehole fields should define cylindrical storage 26 volumes with diameters twice their height , and depth should not exceed 100 m . 27 Keywords 28 Energy storage , geothermal probe , heat transfers , modelling , BTES , dry rock , solar 29 energy , SOLARGEOTHERM 30 Nomenclature 31 ATES Aquifer Thermal Energy Storage 32 BTES Borehole Thermal Energy Storage 33 Cp heat capacity , J . kg – 1 . K – 1 34 density , kg . m – 3 35 thermal conductivity , W . m – 1 . K – 1 36 HDPE High Density PolyEthylene 37 HTF Heat Transfer Fluid 38 m ‘ flow rate , kg . s – 1 39 r radius , m 40 R thermal resistance , m . K . W – 1 41 t time , s 42 T temperature , °C or K 43 z depth , m 44 Subscript 45 b bentonite 46 in inlet 47 ext exterior 48 int interior 49 50