# CiteSpace-Based Bibliometric Review of Pickup and Delivery Problem from 1995 to 2021
## 4 Visualization Results and Bibliometric Analysis Based on CiteSpace
### Reference Analysis
#### 4.5.2 Reference Co-Citation Clustering Analysis
Cluster #5 (traffic congestion) focuses on assessing the negative effects on vehicle routes and schedules caused by congestion on roads, such as economic cost, wasted time, pollution emission, and public health. Actually, traffic congestion is always time-varying, i.e., the average travel speed on routes can transition from one phase to another in an anticipated trend. [PERSON] et al. [74] presented a dynamic memetic algorithm to solve the PDP considering congestion in several time windows. [PERSON] et al. [75] proposed a new adaptive predictive control model for dynamic PDP, which is characterized by the hybrid consideration of additional costs caused by unknown requests and uncertain travel time caused by time-varying congestion. Because a growing number of people choose private car as their transportation, the traffic systems of some developed cities are already overburdened; hence, how to balance the benefits of capital agglomeration and the downsides of congestion is a crucial issue that needs to be taken into account in urban development. Nowadays, with the upsurge of sharing economy and intelligent transportation systems, Cluster #9 (ridesharing) is advertised as an effective and eco-friendly way to mitigate traffic congestion, and the automated matching between drivers and passengers in dynamic ridesharing system can be seen as the DARP. So far, most of the literatures related to ridesharing matching problem seeks to match drivers and riders at a higher accuracy or complete the orders of passengers with a minimum fleet scale. [PERSON] et al. [76] developed a hybrid algorithm to acquire the optimal matching scheme and route choice for the ridesharing matching problem considering space-time window, flexible pickup locations, and flexible delivery locations. Meanwhile, the results of numerical experimentation showed that the introduction of flexible service strategy can increase the ratio of passengers-vehicles matching without additional system costs. [PERSON] and [PERSON] [77] designed an ALNS algorithm to solve the dynamic ridesharing problem involving the redistribution of previously scheduled passengers, and found that the loss of total profit is less than 20% compared to the case where no reassignment of scheduled passengers is required. A low-carbon non-motorized traffic mode emerging with smart payment and sharing economy is bike-sharing, which is expected to ease the traffic congestion in short-distance trips through increasing the usage of bicycles. Specifically, the research topic relevant to bike-sharing and belonging to the extensive class of PDP is termed as \"bike-sharing rebalancing problem\" (BRP), which attempts to determine minimum cost routes for a fleet of capacitated vehicles to redistribute bikes among stations in bike-sharing system [78]. The research findings in this area can be divided into two main categories, namely, static situation and dynamic situation. Static situation supposes the rebalancing process occurs when the bike-sharing system is closed, that is, no consideration of the customer's real-time demand during allocation. In contrast, dynamic rebalancing problem needs to consider the daily operational demands in bike-sharing system when redistributing bikes among stations. [PERSON] et al. [79] incorporated the depot inventory cost into the objective function of static bike-sharing rebalancing problem, and reformulated the whole problem into a MILP. To solve it more efficiently, they proposed an improved general VNS algorithm that integrated the improvement strategy of basic VNS algorithm with the best neighbor strategy of general VNS algorithm. [PERSON] and [PERSON] [80] formulated a revised model for static repositioning problem concerning multiple capacitated vehicles, total travel time cost, and penalty cost, which can be viewed as an in-depth exploration of the model proposed in [81].