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Building smart transportation services in urban cities has become a worldwide problem owing to the rapidly increasing global population and the development of Internet‐of‐Things applications. Traffic congestion and environmental concerns can be alleviated by sharing mobility, which reduces the number of vehicles on the road network. The taxi‐parcel sharing problem has been considered as an efficient planning model for people and goods flows. In this paper, we enhance the functionality of a current people‐parcel taxi sharing model. The adapted model analyzes the historical request data and predicts the current service demands. We then propose two novel online routing algorithms that construct optimal routes in real‐time. The objectives are to maximize (as far as possible) both the parcel delivery requests and ride requests while minimizing the idle time and travel distance of the taxis. The proposed online routing algorithms are evaluated on instances adapted from real Cabspotting datasets. After implementing our routing algorithms, the total idle travel distance per day was 9.64% to 12.76% lower than that of the existing taxi‐parcel sharing method. Our online routing algorithms can be incorporated into an efficient smart shared taxi system.

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In the real world, multi-objective problems(MOPs) are relatively common in optimization in the areasof design, planning, decision support... In fact, problemsinclude two or many objectives, there is a class of problemscalled expensive problems that are problems with complexmathematical models, large computational costs,... Theycan not be solved by normal techniques, they are usually tobe solved with techniques such as simulation, decomposing,problem transformation. In particular, using a surrogatemodel with Kriging, neuron networks techniques in combination with an evolutionary algorithm is a subtle choice,with many positive results, being studied and applied inpractice. However, the use of a surrogate model withKriging, neuron networks combining selection strategy,sampling... can reduce the robustness of the algorithmsduring the search. This paper analyzes the issues affectingthe robustness of the multi-objective evolutionary algorithms (MOEAs) using surrogate models and suggests theuse of a guidance technique to increase the robustness ofthe algorithm, through analysis, experiment and results arecompetitive and effective to improve the quality of MOEAsusing a surrogate model to solve expensive problems.