氏名　Pairoj Raothanachonkun
学位の種類　博士（工学）
学位記番号　博甲第443号
学位授与の日付　平成19年9月30日
学位論文題目　Estimation of Truck Origin-destination Matrix from Traffic Counts and Commodity Flow　（観測リンク交通量と物質流動を用いた貨物車OD交通量の推計）
論文審査委員
　主査　准教授　佐野　可寸志
　副査　教授　松本　昌二
　副査　教授　中出　文平
　副査　准教授　宮木　康幸
　副査　准教授　樋口　秀

• 論文目次
• 論文要旨
• 論文審査の結果の要旨

［平成19（2007）年度博士論文題名一覧］ ［博士論文題名一覧］に戻る．

Acknowledgement　p.i
Abstract　p.iii
List of Figures　p.xi
List of Tables　p.xiii
1.　Intorduction　p.1
　1.1　General Background　p.1
　1.2　Problem Statement　p.3
　1.3　Research Objectives　p.6
　1.4　Scope and Limitations　p.6
　1.5　Contributions　p.7
　1.6　Organization　p.8
2.　Literature Review　p.9
　2.1　Introduction　p.9
　2.2　O-D Matrix Estimation Using Vehicle-Trip=Based Models　p.10
　2.2.1 Previous Works　p.11
　2.2.2 O-D Matrix Estimation from Link Traffic Counts　p.12
　2.2.3 General Forms of O-D Matrix Estimation Model　p.12
　2.2.4 O-D Matrix Estimation Model in Uncongested Network　p.13
　2.2.5 O-D Matrix Estimation Models in Congested Networks　p.16
　2.2.6 Other O-D Matrix Estimation Models　p.21
　2.3　O-D Matrix Estimation Using Commodity-Based Models　p.27
　2.3.1 Classification of Commodity Movement　p.27
　2.3.2 Data for Analysis in Commodity Movement　p.29
　2.3.3 Conversion of Commodity Flows into Truck Trips　p.30
　2.4　Truck Routing　p.30
　2.4.1 Truck Route Assignment Model　p.32
　2.4.2 Truck Route Plan　p.33
　2.4.3 Vehicle Routing Problems　p.34
　2.5　Truck Interview Survey　p.35
　2.6　Commodity Demand and Truck Trip Forecasting　p.37
3.　Conceptual Framework　p.47
　3.1　Introduction　p.47
　3.2　Overview of Methodology Framework　p.47
　3.2.1 Estimation of Truck O-D Matrices from Commodity Flows　p.48
　3.2.2 Estimation of Truck O-D Matrices from Link Traffic Couonts　p.49
　3.2.3 Estimation of Truck O-D Matrices from Link Traffic Counts and Commodity Flows　p.49
4.　Estimation of Truck O-D Matrices from Commodity Flows　p.51
　4.1　Introduction　p.51
　4.2　Overview of Truck Movement and Commodity movement　p.52
　4.3　Methodology of Commodity-Based Approach　p.55
　4.3.1 Previous Approaches　p.55
　4.3.2 Overall Concept　p.55
　4.3.3 Parameter Calibrations　p.57
　4.3.4 Estimation of Loaded Trips Based on Round Trips　p.58
　4.3.5 Estimation of Loaded Trips Based on Trip Chains　p.58
　4.3.5.1 Estimation of Loaded Trips Based on Trip Chains　p.58
　4.3.5.2 Estimation of Loaded Trips Based on Trip Chains　p.58
　4.3.5.2 Estimation of Loaded Trips Based on Trip Chains　p.58
　4.3.6 Empty Trips Estimation　p.65
　4.3.7 Summation of Truck Trips　p.66
　4.3.8 Performance of the Model　p.66
　4.4　Data and Calibrated Parameters　p.67
　4.4.1 Study Area　p.67
　4.4.2 Classifications of Data　p.67
　4.4.3 Shipment Characteristics　p.69
　4.4.3.1 Maximum Working Hours od a Driver　p.69
　4.4.3.2 Maximum Number of Trip Sequence of One Trip Chain　p.70
　4.4.3.3 Relationship of Unloading Weight and Distance　p.70
　4.4.3.4 Average Staying Time Spent at Customer Locations　p.70
　4.5　Result and Discussions　p.71
　4.5.1 Results of Native Proportional-Based Model　p.71
　4.5.2 Result of Probability-Based Model　p.73
　4.5.3 Results of Sequence-Based Model　p.74
　4.5.4 Sensitivity Analysis of Sequence-Based Model　p.75
　4.6　Conclusions　p.75
5.　Estimation of Truck O-D Matrices from Link Traffic Counts　p.77
　5.1　Introduction　p.77
　5.2　Model Formulation　p.79
　5.2.1 Traffic Assignment Formulation　p.79
　5.2.2 O-D Matrics Estimation Formulation　p.81
　5.2.3 Solution Algorithm Based on the GA　p.82
　5.2.4 Statistical Test　p.85
　5.3　Numerical Examples　p.86
　5.4　Results and Discussions　p.88
　5.5　Conclusions　p.92
6.　Estimation of Truck O-D Matrices from Link Traffic Counts and Commodity Flows　p.93
　6.1　Introduction　p.93
　6.2　Methodology　p.93
　6.2.1 Overall Concepts　p.93
　6.2.2 Data Generation　p.94
　6.2.3 Estimated Initial Truck O-D from Commodity Flows　p.97
　6.2.4 O-D Estimation Formulations Based on the GA　p.98
　6.2.5 Performance of the Model　p.99
　6.3　Examples of Simulation Study　p.100
　6.3.1 Numerical Network　p.100
　6.3.2 Target O-D Matrices　p.103
　6.3.3 Number of Trips　p.103
　6.3.4 Data Validation　p.103
　6.4　Results and Discussions　p.105
　6.5　Conclusions　p.108
7.　Conclutions and Recommendations　p.111
　7.1　Conclutions　p.111
　7.1.1 Commodility-Based Approach　p.111
　7.1.2 Vehicle-Trip-Based Approach　p.112
　7.1.3 Combination of Vehicle-Trip-Based and Commodity-Based Approaches　p.113
　7.2　Recommendations for Furthur Studies　p.113
References　p.115
A Characteristics of Survey Data　p.125
B Average Staying Time　p.127
C List of Achievements　p.129

　Truck origin-destination （O-D） matrix is necessary for urban goods movement planning and there are not many researches that estimate truck O-D matrix together with passenger car O-D matrix. The truck O-D matrix is generally determined by either vehicle-trip-based or commodity-based approaches, although the former cannot distinguish between loaded and empty trips and does not characterize the shipments. The main contribute of this study is to estimate truck O-D matrix from vehicle-trip-based, commodity-based and combination of these approaches.
　This study firstly estimates O-D matrices of light heavy trucks based on the flows of commodities in the Tokyo metropolitan area. Three major concepts are proposed in this methodology. Firstly, the truck trip O-D is estimated based on the commodity approach since it can utilize the characteristics of the shipments. Secondly, the main contribution of the model is its ability to estimate both loaded and empty trips by modeling the truck movements as round trips and trip chains. The O-D of truck movement, particularly the movement of loaded trips in round trips or zero-order trip chains, is similar to that of thecommodity flows. However, both movements have relatively different O-D when the loaded trips travel from one origin to many destinations or as part of nthordertrip chain. Finally the trip chain is modeled based on characteristics such as average payload, adjacent zones, and the commodity O-D providing the most attractive zones traveled by trucks. The performance of this model is demonstrated using the mean square error between the estimated and observed truck O-D matrices. The model concept is the applied to lightweight products obtained from the food industry. A sensitivity analysis is also performed to demonstrate the significant of shipment characteristics. In conclusion, the proposed concept enhances the trip chain behavior and provides better results than the model without trip chain behavior.

　Secondly, this study proposes a model based on the vehicle-trip-based approachthat estimates multiclass O-D matrices consisting of passenger car and truck. A genetic algorithm （GA） is implemented to search the optimal multiclass O-D matrices. This study also suggests using the average value from running the GA several times because of fluctuation of the results. The GA concept applies on anumerical network in various scenarios to demonstrate performances of the model.The findings demonstrate that the proposed model provides better results than the others, especially when the objective function does not rely on the initial O-D matrices.
　Finally, this study estimates truck O-D matrices from the combination of linktraffic counts and commodity flows to increase the accuracy of the estimated O-Dresult. A simulation study is demonstrated and performed on the Sioux Falls network that is a well-known network. Simulated data consisting of commodity flows and link traffic counts on the Sioux falls network is generated and validated with the data from the Tokyo to estimate the reliable initial O-D matrices of trucks. The commodity-based approach is firstly applied to estimate the reliable initial O-D matrices of trucks. The GA concept is then performed that the proposed concept provides satisfied results by combining vehicle-trip-based and commodity-based approaches together.

　本論文は，「Estimation of Truck Origin-destination Matrix from Traffic Counts and Commodity Flow （観測リンク交通量と物質流動を用いた貨物車OD交通量の推計）」と題し，7章より構成されている．
　第1章「Introduction」では，既存の貨物車OD交通量の推定方法が持つ問題点について延べ，本研究の必要性と適用範囲を示している．
　第2章「Literature Review」では，vehicle-trip-based approachとcommodity-based approachに分けて，一般的な貨物車ODの推定方法を示している．
　第3章「Conceptual Framework」では，本研究で提案する貨物車OD交通量の推定方法のフレームワークを記述している．
　第4章「Estimation of Truck O-D Matrices from Commodity Flows」では，OD貨物量から，貨物車OD交通量の推定方法と，その東京都市圏への適用例を記述している．
　第5章「Estimation of Truck O-D Matrices from Link Traffic Counts 」では，観測リンク交通量とGAアルゴリズムを使用した貨物車のOD交通量の推定方法と，その数値計算結果を記述している．
　第6章「Estimation of Truck O-D Matrices from Link Traffic Counts and CommodityFlows」では，第4章と第5章で構築した二つのた貨物車OD交通量の推定方法を同時に適用した際の，数値計算例を示し，本研究で提案された手法の有効性を確認している．
　第7章「Conclusions and Recommendations」では，各省で示したモデルの特徴を概観し，今後の課題をまとめている．
　以上のように，新たに提案された2種類の貨物車OD交通量の推計方法を組み合わせることにより，より精度の高い貨物車OD交通量を求めることが可能となった．よって，本論文は工学上および工業上貢献するところが大きく，博士（工学）の学位論文として十分な価値を有するものと認める．