The prediction and behavioural analysis of travel mode choice and purpose are critical for transport planning and have attracted increasing interest in research. Traditionally, the prediction of travel mode choice and trip purpose has been tackled separately, which fail to fully leverage the shared information between travel mode and purpose. This study addresses this gap by proposing a multitask learning deep neural network framework (MTLDNN) to jointly predict mode choice and purpose. We empirically evaluate and validate this framework using the household travel survey data in Greater London, UK. The results show that this framework has significantly lower cross-entropy loss than multinomial logit models (MNL) and single-task-learning deep neural network models (STLDNN). On the other hand, the predictive accuracy of MTLDNN is similar to STLDNN and is significantly higher than MNL. Moreover, in terms of behaviour analysis, the substitution pattern and choice probability of MTLDNN regarding input variables largely agree with MNL and STLDNN. This work demonstrates that MTLDNN is efficient in utilising the information shared by travel mode choice and purpose, and is capable of producing behaviourally reasonable substitution patterns across travel modes. Future research would develop more advanced MTLDNN frameworks for travel behaviour analysis and generalise MTLDNN to other travel behaviour topics
