An Adaptive Route Planning Method of Connected Vehicles for Improving the Transport Efficiency

In recent years, the route-planning problem has gained increased interest due to the development of intelligent transportation systems (ITSs) and increasing traffic congestion especially in urban areas. An independent route-planning strategy for each in-vehicle terminal improves its individual travel efficiency. However, individual optimal routes pursue the maximization of individual benefit and may contradict the global benefit, thereby reducing the overall transport efficiency of the road network. To improve traffic efficiency while considering the travel time of individual vehicles, we propose a new dynamic route-planning method by innovatively introducing a bidding mechanism in the connected vehicle scenario for the first time. First, a novel bidding-based dynamic route planning is proposed to formulate vehicle routing schemes for vehicles affected by congestion via the bidding process. Correspondingly, a bidding price incorporating individual and global travel times was designed to balance the travel benefits of both objectives. Then, in the bidding process, a new local search algorithm was designed to select the winning routing scheme set with the minimum bidding price. Finally, the proposed method was tested and validated through case studies of simulated and actual driving scenarios to demonstrate that the bidding mechanism would be conducive to improving the transport efficiency of road networks in large-scale traffic flow scenarios. This study positively contributes to the research and development of traffic management in ITSs.

Publicly Verifiable M + 1st-Price Auction Fit for IoT with Minimum Storage

In an M + 1 st-price auction, all bidders submit their bids simultaneously, and the M highest bidders purchase M identical goods at the M + 1 st bidding price. Previous research is constructed based on trusted managers such as a trusted third party (TTP), trusted mix servers, and honest managers. All of the previous auctions are not fit for edge-assisted IoT since they need TTP. In this paper, we formalize a notion of commutative bi-homomorphic multiparty encryption and achieve no-TTP M + 1 -st auction based on blockchain with public verifiability. Our M + 1 st auction guarantees financial fairness, robustness, and correctness without TTP and is secure under a malicious model for the first time. Our M + 1 st auction can be executed over a distributed network and is thus fit for edge-assisted IoT. Furthermore, our formalized commutative bi-homomorphic multiparty encryption can be used in various applications for edge-assisted IoT, which needs to protect privacy and correctness.

Bundling Decisions for Selling Multiple Items in Online Auctions

Fueled by the widespread use of the internet, more and more ordinary people have now become merchandise sellers who sell their own possessions, such as antique collections and limited souvenirs, to buyers who are interested in such goods via online auctions. This study examines the decision making related to the bidding strategies used in online auctions by both sellers and buyers. When selling goods for which there is a limited supply, sellers consider whether to sell the single homogenous items in multiple, simultaneous auctions or all the items in a single auction. Moreover, when selling heterogeneous but associated goods, sellers may decide to bundle the items for sale or not with an aim of increasing the potential buyers’ willingness to make a purchase. We investigate the effects that various factors related to the bidding strategies used in online auctions, such as the base price and duration of the auction determined by the seller and the bidding price decided by the buyer, have on the seller’s profit, and the utilities of both parties are considered to derive the equilibrium solutions. This study contributes to the literature by proposing an online auction framework that focuses more on individual sellers selling a limited quantity of items with an aim to establish a favorable online auction for both sellers and buyers and earn more profits for sellers. The results show that the base prices and direct purchase prices should be unestablished to achieve the most attractive characteristics of online auctions, which would encourage more buyers to freely place bids. As a result, the bidding items would have more chances to be eventually obtained by the buyer who places the highest bid, which, thus, maximizes the seller’s profit.

Determining the Operator for the Public Toll Road

After the BOT road operation contract expires, generally, the road will be transferred to the government, and then the government operates the road independently without charging costs from its users. Facing the huge amount of the operation cost, Chinese government tends to continue to charge the road users to guarantee the high quality of road operation. Then, the government will have to decide whether a private firm or government itself would be suitable to operate the road. A model is presented for decision-making through balancing interests between the government and the private firm with an introduction of an intermediate variable, i.e., bidding price. Three scenarios are investigated in the model, including the optimization of government operation, the optimization of private firm operation, and government operation with an improper decision of the intermediate variable. Improper intermediate variable will result in a higher toll charged by the government than by a private firm. The method of avoiding an improper decision is investigated. The result shows that the intermediate variable should be determined to be the government operation cost, based on which the private operator could be chosen, if available. With consideration of the private operator’s profit to be guaranteed by the government, the maximum subsidy should be equal to the minimum private operator’s profit to be disclosed when the contract is signed.

A hybrid mathematical programming model and statistical approach for bidding price decision in construction projects

Bidding price decision is a key issue for the contractors and construction companies. The success/failure of the contractors in competitive biddings is directly dependent on their bidding strategy. This paper aims to develop a hybrid statistical and mathematical modeling approach for determining the optimum bidding price in construction projects. By statistical analysis of historical data, some uncertain parameters like the number of competitors and the cost of the project are estimated. Then, a scenario-based mathematical model for bidding price decision is proposed. In order to present a model in more accordance with the real-world situations, factors like risk, minimum acceptable rate of return (MARR) and opportunistic behavior are taken into account. In order to achieve an insensitive solution to the change in the realization of the input data from the scenarios, a robust mathematical model is used. The performance of the model is evaluated through some numerical problems. Furthermore, sensitivity analysis of the key parameters and robustness evaluation of the model against uncertain parameters are conducted. To evaluate the model's effectiveness in real-world situations, a case study is analyzed by the proposed approach. Numerical results show that the proposed approach reduces the cost estimation errors and increases the average expected profit, which validates the applicability of the model in a real-world situation.