Time-dependent Hyperstar algorithm for robust vehicle navigation in time-dependent stochastic road networks.

The vehicle navigation problem studied in Bell (2009) is revisited and a time-dependent reverse Hyperstar algorithm is presented. This minimises the expected time of arrival at the destination, and all intermediate nodes, where expectation is based on a pessimistic (or risk-averse) view of unknown link delays. This may also be regarded as a hyperpath version of the Chabini and Lan (2002) algorithm, which itself is a time-dependent A* algorithm. Links are assigned undelayed travel times and maximum delays, both of which are potentially functions of the time of arrival at the respective link. The driver seeks probabilities for link use that minimise his/her maximum exposure to delay on the approach to each node, leading to the determination of the pessimistic expected time of arrival. Since the context considered is vehicle navigation where the driver is not making repeated trips, the probability of link use may be interpreted as a measure of link attractiveness, so a link with a zero probability of use is unattractive while a link with a probability of use equal to one will have no attractive alternatives. A solution algorithm is presented and proven to solve the problem provided the node potentials are feasible and a FIFO condition applies for undelayed link travel times. The paper concludes with a numerical example.

Interactive support effects on career agency and occupational engagement among young adults.

The PIC model by Gati and Asher describes three career decision making stages: pre-screening, in-depth exploration, and choice of career options. We consider the role that three different forms of support (general career support by parents, emotional/instrumental support, and informational support) may play for young adults in each of these three decision-making stages. The authors further propose that different forms of support may predict career agency and occupational engagement, which are important career decision precedents. In addition, we consider the role of personality traits and perceptions (decision-making window) on these two outcomes. Using an online survey sample (N = 281), we found that general career support was important for career agency and occupational engagement. However, it was the combination of higher general career support with either emotional/instrumental support or informational support that was found to lead to both greater career agency and higher occupational engagement. Personality also played a role: Greater proactivity also led to greater occupational engagement, even when there was little urgency for participants to make decisions (window of decision-making was wide open and not restricted). In practical terms, the findings suggest that the learning required in each of the three PIC processes (pre-screening, in-depth exploration, choice of career options may benefit when the learner has access to the three support measures.