Forecasting freeway link travel times with a multilayer feedforward neural network D Park, LR Rilett Computer‐Aided Civil and Infrastructure Engineering 14 (5), 357-367, 1999 | 433 | 1999 |
Forecasting multiple-period freeway link travel times using modular neural networks D Park, LR Rilett Transportation research record 1617 (1), 163-170, 1998 | 301 | 1998 |
Spectral basis neural networks for real-time travel time forecasting D Park, LR Rilett, G Han Journal of Transportation Engineering 125 (6), 515-523, 1999 | 297 | 1999 |
Review of pollutants in urban road dust and stormwater runoff: part 1. Heavy metals released from vehicles HM Hwang, MJ Fiala, D Park, TL Wade International Journal of Urban Sciences 20 (3), 334-360, 2016 | 249 | 2016 |
Dynamic multi-interval bus travel time prediction using bus transit data H Chang, D Park, S Lee, H Lee, S Baek Transportmetrica 6 (1), 19-38, 2010 | 165 | 2010 |
Direct forecasting of freeway corridor travel times using spectral basis neural networks LR Rilett, D Park Transportation Research Record 1752 (1), 140-147, 2001 | 148 | 2001 |
Application of association rules mining algorithm for hazardous materials transportation crashes on expressway J Hong, R Tamakloe, D Park Accident Analysis & Prevention 142, 105497, 2020 | 108 | 2020 |
Dynamic and stochastic shortest path in transportation networks with two components of travel time uncertainty P Pattanamekar, D Park, LR Rilett, J Lee, C Lee Transportation Research Part C: Emerging Technologies 11 (5), 331-354, 2003 | 108 | 2003 |
An effects analysis of logistics collaboration in last-mile networks for CEP delivery services H Park, D Park, IJ Jeong Transport Policy 50, 115-125, 2016 | 104 | 2016 |
Estimation of value of travel time reliability D Nam, D Park, A Khamkongkhun Journal of Advanced Transportation 39 (1), 39-61, 2005 | 88 | 2005 |
Who produces the most CO2 emissions for trips in the Seoul metropolis area? J Ko, D Park, H Lim, IC Hwang Transportation Research Part D: Transport and Environment 16 (5), 358-364, 2011 | 79 | 2011 |
Identifying multiple and reasonable paths in transportation networks: A heuristic approach D Park, LR Rilett Transportation Research Record 1607 (1), 31-37, 1997 | 78 | 1997 |
Review of pollutants in urban road dust: Part II. Organic contaminants from vehicles and road management HM Hwang, MJ Fiala, TL Wade, D Park International Journal of Urban Sciences 23 (4), 445-463, 2019 | 64 | 2019 |
Solving the bicriteris traffic equilibrium problem with variable demand and nonlinear path costs A Chen, J Oh, D Park, W Recker Applied Mathematics and Computation 217 (7), 3020-3031, 2010 | 58 | 2010 |
Factors affecting motorcycle crash casualty severity at signalized and non-signalized intersections in Ghana: Insights from a data mining and binary logit regression approach R Tamakloe, S Das, EN Aidoo, D Park Accident Analysis & Prevention 165, 106517, 2022 | 53 | 2022 |
Relation model describing the effects of introducing RFID in the supply chain: evidence from the food and beverage industry in South Korea OK Ha, YS Song, KY Chung, KD Lee, D Park Personal and Ubiquitous Computing 18, 553-561, 2014 | 53 | 2014 |
Estimation of the non-greenhouse gas emissions inventory from ships in the port of Incheon H Lee, D Park, S Choo, HT Pham Sustainability 12 (19), 8231, 2020 | 51 | 2020 |
Discovering insightful rules among truck crash characteristics using apriori algorithm J Hong, R Tamakloe, D Park Journal of advanced transportation 2020 (1), 4323816, 2020 | 49 | 2020 |
A copula-based approach for jointly modeling crash severity and number of vehicles involved in express bus crashes on expressways considering temporal stability of data R Tamakloe, J Hong, D Park Accident Analysis & Prevention 146, 105736, 2020 | 48 | 2020 |
Estimating trade-off among logistics cost, CO2 and time: A case study of container transportation systems in Korea D Park, NS Kim, H Park, K Kim International Journal of Urban Sciences 16 (1), 85-98, 2012 | 41 | 2012 |